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             Please use the previous external buffer!`)}else w=tA();return this.storageCache.set(w,{gpuData:{id:w,type:0,buffer:n},originalSize:u}),Pt("verbose",()=>`[WebGPU] GpuDataManager.registerExternalBuffer(size=${u}) => id=${w}, registered.`),w}unregisterExternalBuffer(n){n!==void 0&&(this.storageCache.delete(n),Pt("verbose",()=>`[WebGPU] GpuDataManager.unregisterExternalBuffer() => id=${n}`))}create(n,u=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST){let h,w=rs(n),B=(u&GPUBufferUsage.STORAGE)===GPUBufferUsage.STORAGE,v=(u&GPUBufferUsage.UNIFORM)===GPUBufferUsage.UNIFORM;if(B||v){let F=(B?this.freeBuffers:this.freeUniformBuffers).get(w);h=F&&F.length>0?F.pop():this.backend.device.createBuffer({size:w,usage:u})}else h=this.backend.device.createBuffer({size:w,usage:u});let y={id:tA(),type:0,buffer:h};return this.storageCache.set(y.id,{gpuData:y,originalSize:Number(n)}),Pt("verbose",()=>`[WebGPU] GpuDataManager.create(size=${n}) => id=${y.id}`),y}get(n){return this.storageCache.get(n)?.gpuData}release(n){let u=typeof n=="bigint"?Number(n):n,h=this.storageCache.get(u);if(!h){if(this.storageCache.size===0)return 0;throw new Error("releasing data does not exist")}return Pt("verbose",()=>`[WebGPU] GpuDataManager.release(id=${u}), gpuDataId=${h.gpuData.id}`),this.storageCache.delete(u),this.buffersPending.push(h.gpuData.buffer),h.originalSize}async download(n,u){let h=this.storageCache.get(Number(n));if(!h)throw new Error("data does not exist");await _(this.backend,h.gpuData.buffer,h.originalSize,u)}refreshPendingBuffers(){if(this.buffersPending.length!==0)if(this.backend.sessionStatus==="default"){for(let n of this.buffersPending){let u=Ai.get(n.size);if((n.usage&GPUBufferUsage.STORAGE)===GPUBufferUsage.STORAGE){let h=this.freeBuffers.get(n.size)||[];u===void 0||h.length>=u?n.destroy():h.push(n)}else if((n.usage&GPUBufferUsage.UNIFORM)===GPUBufferUsage.UNIFORM){let h=this.freeUniformBuffers.get(n.size)||[];u===void 0||h.length>=u?n.destroy():h.push(n)}else n.destroy()}this.buffersPending=[]}else{let n=this.capturedPendingBuffers.get(this.backend.currentSessionId);n||(n=[],this.capturedPendingBuffers.set(this.backend.currentSessionId,n));for(let u of this.buffersPending)n.push(u);this.buffersPending=[]}}dispose(){this.freeBuffers.forEach(n=>{n.forEach(u=>{u.destroy()})}),this.freeUniformBuffers.forEach(n=>{n.forEach(u=>{u.destroy()})}),this.storageCache.forEach(n=>{n.gpuData.buffer.destroy()}),this.capturedPendingBuffers.forEach(n=>{n.forEach(u=>{u.destroy()})}),this.storageCache=new Map,this.freeBuffers=new Map,this.freeUniformBuffers=new Map,this.capturedPendingBuffers=new Map}onCreateSession(){this.sessionCount+=1}onReleaseSession(n){let u=this.capturedPendingBuffers.get(n);u&&(u.forEach(h=>{h.destroy()}),this.capturedPendingBuffers.delete(n)),this.sessionCount-=1,this.sessionCount===0&&(Pt("warning",()=>"[WebGPU] Clearing webgpu buffer cache"),this.storageCache.forEach(h=>{h.gpuData.buffer.destroy()}),this.storageCache=new Map)}},Oe=(...n)=>new Be(...n)}),qr=Pe(()=>{Ve=class{constructor(n){Object.assign(this,n)}get cacheKey(){return this.key||(this.key=Object.getOwnPropertyNames(this).sort().map(n=>`${this[n]}`).join(";")),this.key}},$e=n=>new Ve(n)}),St=Pe(()=>{fA=class{static calcMatMulShape(n,u){return n[1]!==u[0]?void 0:[n[0],u[1]]}},NA=class{static calcShape(n,u,h=!1){let w=n.length,B=u.length;if(w===0)return u;if(B===0)return n;let v=Math.max(n.length,u.length),y=new Array(v);if(h){if(w<2||B<2)return;let F=fA.calcMatMulShape([n[w-2],n[w-1]],[u[B-2],u[B-1]]);if(F===void 0)return;[y[v-2],y[v-1]]=F}for(let F=h?3:1;F<=v;F++){let N=w-F<0?1:n[w-F],Y=B-F<0?1:u[B-F];if(N!==Y&&N>1&&Y>1)return;let te=Math.max(N,Y);if(N&&Y)y[v-F]=Math.max(N,Y);else{if(te>1)return;y[v-F]=0}}return y}static isValidBroadcast(n,u){let h=n.length,w=u.length;if(h>w)return!1;for(let B=1;B<=h;B++)if(n[h-B]!==1&&n[h-B]!==u[w-B])return!1;return!0}},He=class ec{static size(u){return ec.getSizeFromDimensionRange(u,0,u.length)}static convertShape(u,h=4){let w=u.length;if(w===0)return[];let B=new Array(w),v=w-1;for(;v>=0;){if(u[v]%h==0){B[v]=u[v]/h;break}if(h%u[v]!=0)throw new Error("cannot convert shape");B[v]=1,h/=u[v],v--}for(v--;v>=0;v--)B[v]=u[v];return B}static sizeFromDimension(u,h){if(h<0||h>u.length)throw new Error(`invalid dimension of ${h} for sizeFromDimension as Tensor has ${u.length} dimensions.`);return ec.getSizeFromDimensionRange(u,h,u.length)}static sizeToDimension(u,h){if(h<0||h>u.length)throw new Error(`invalid dimension of ${h} for sizeToDimension as Tensor has ${u.length} dimensions.`);return ec.getSizeFromDimensionRange(u,0,h)}static getSizeFromDimensionRange(u,h,w){let B=1;for(let v=h;v<w;v++){if(u[v]<0)throw new Error("cannot get valid size from specified dimension range. Most likely the range contains negative values in them.");B*=Number(u[v])}return B}static computeStrides(u){let h=u.length;if(h===0)return[];if(h===1)return[1];let w=new Array(h);w[h-1]=1,w[h-2]=u[h-1];for(let B=h-3;B>=0;--B)w[B]=w[B+1]*u[B+1];return w}static normalizeAxis(u,h){if(u<-h&&u>=h)throw new Error("unsupported axis for this operation.");return u<0?u+h:u}static normalizeAxes(u,h){return u.map(w=>this.normalizeAxis(w,h??u.length))}static sortBasedOnPerm(u,h){return h?h.map(w=>u[w]):u.slice().reverse()}static padShape(u,h){let w=u.length;return u.map((B,v)=>B+h[v]+h[v+w])}static areEqual(u,h){return u.length===h.length&&u.every((w,B)=>w===h[B])}},VA=class w1{static adjustPoolAttributes(u,h,w,B,v,y){if(!u&&w.length!==h.length-2)throw new Error("length of specified kernel shapes should be 2 less than length of input dimensions");if(u)for(let F=0;F<h.length-2;F++)F>=w.length?w.push(h[F+2]):w[F]=h[F+2];for(let F=0;F<w.length;F++)if(F<B.length){if(B[F]<0)throw new Error("strides should be greater than or equal to 1")}else B.push(1);for(let F=0;F<w.length;F++)if(F<v.length){if(v[F]<0)throw new Error("dilations should be greater than or equal to 1")}else v.push(1);for(let F=0;F<2*w.length;F++)if(F<y.length){if(y[F]<0)throw new Error("pad should be greater than or equal to 1")}else y.push(0);for(let F=0;F<w.length;F++){if(w[F]<=0)throw new Error("kernel shapes need to be greater than 0");if(y[F]>=w[F]||y[F+w.length]>=w[F])throw new Error("pads should be smaller than kernel")}}static adjustPadsBasedOnAutoPad(u,h,w,B,v,y,F){if(F){if(v.length!==2*(u.length-2))throw new Error("length of pads should be twice the length of data dimensions");if(h.length!==u.length-2)throw new Error("length of strides should be the length of data dimensions");if(B.length!==u.length-2)throw new Error("length of kernel shapes should be the length of data dimensions");for(let N=0;N<u.length-2;N++)w1.adjustPadAndReturnShape(u[N+(y?1:2)],h[N],w[N],B[N],v,N,N+u.length-2,F)}}static computePoolOutputShape(u,h,w,B,v,y,F){if(h.length<=0)throw new Error("input shape must be of size greater than 0");let N=[h[0],h[1]];return w1.computeShapeHelper(u,h,N,w,B,v,y,F),N}static computeConvOutputShape(u,h,w,B,v,y,F){if(u.length<=0||h.length<=0)throw new Error("invalid input tensor dims or invalid filter tensor dims");let N=[u[0],h[0]];return w1.computeShapeHelper(!1,u,N,w,B,v,y,F),N}static computeShapeHelper(u,h,w,B,v,y,F,N){if(u)for(let Y=0;Y<h.length-2;Y++)w.push(1);else for(let Y=0;Y<h.length-2;Y++)w.push(w1.adjustPadAndReturnShape(h[Y+2],B[Y],v[Y],y[Y],F,Y,Y+h.length-2,N))}static adjustPadAndReturnShape(u,h,w,B,v,y,F,N){let Y=w*(B-1)+1;if(!N||N==="NOTSET")return Math.floor((u+v[y]+v[F]-Y)/h+1);switch(N){case"VALID":return v[y]=0,v[F]=0,Math.floor((u-Y)/h+1);case"SAME_LOWER":case"SAME_UPPER":if(w!==1)throw new Error("Dilation not supported for SAME_UPPER or SAME_LOWER");{let te=((u+h-1)/h-1)*h+B-u;return v[y]=Math.floor(N==="SAME_LOWER"?(te+1)/2:te/2),v[F]=te-v[y],Math.floor((u+te-B)/h+1)}default:throw new Error("Unsupported AutoPad type")}}},bA=class{static getShapeOfGemmResult(n,u,h,w,B){if(n.length!==2||h.length!==2)throw new Error("shape need to be of size 2");let v,y,F;u?(v=n[1],y=n[0]):(v=n[0],y=n[1]);let N=-1;if(w?(F=h[0],N=1):(F=h[1],N=0),h[N]!==y)throw new Error("dimension mismatch");if(v<=0||F<=0||y<=0)throw new Error("invalid shape specified");if(B&&!NA.isValidBroadcast(B,[v,F]))throw new Error("gemm: invalid bias shape for broadcast");return[v,F,y]}},Yt=-34028234663852886e22,Vt=34028234663852886e22}),Rt=Pe(()=>{Dt(),St(),cr=64,sr=(n,u)=>{if(u===3)throw new Error("vec3 has same alignment as vec4, use vec4 instead");switch(Number(n)){case 10:return u>1?`vec${u}<f16>`:"f16";case 1:return u>1?`vec${u}<f32>`:"f32";case 6:return u>1?`vec${u}<i32>`:"i32";case 12:return u>1?`vec${u}<u32>`:"u32";case 7:if(u>1)throw new Error("currently not supported vecX of uint64 yet");return["vec2<u32>","i32"];case 13:if(u>1)throw new Error("currently not supported vecX of uint64 yet");return["vec2<u32>","u32"];case 9:if(u!==4)throw new Error("bool must be vec4");return["u32","vec4<bool>"];case 22:return"i32";case 21:return"u32";default:throw new Error(`Unknown data type: ${n}`)}},jt=(n,u=1)=>{let h=sr(n,u);return typeof h=="string"?h:h[0]},jA=(n,u=1)=>{let h=sr(n,u);return typeof h=="string"?h:h[1]},KA=(...n)=>{let u=[];return n.forEach(h=>{h.length!==0&&u.push({type:12,data:h},{type:12,data:He.computeStrides(h)})}),u},Jt=n=>n%4==0?4:n%2==0?2:1,La=(n="f32",u,h="0")=>u&&u!==1?`vec${u}<${n}>(${h})`:`${n}(${h})`,fa=(n,u,h)=>n==="f32"?h:u===1?`f32(${h})`:`vec${u}<f32>(${h})`,ir=(n,u)=>u===4?`(${n}.x + ${n}.y + ${n}.z + ${n}.w)`:u===2?`(${n}.x + ${n}.y)`:u===3?`(${n}.x + ${n}.y + ${n}.z)`:n,rt=(n,u,h,w)=>n.startsWith("uniforms.")&&h>4?typeof u=="string"?w==="f16"?`${n}[(${u}) / 8][(${u}) % 8 / 4][(${u}) % 8 % 4]`:`${n}[(${u}) / 4][(${u}) % 4]`:w==="f16"?`${n}[${Math.floor(u/8)}][${Math.floor(u%8/4)}][${u%8%4}]`:`${n}[${Math.floor(u/4)}][${u%4}]`:h>1?`${n}[${u}]`:n,dr=(n,u,h,w,B)=>{let v=typeof h=="number",y=v?h:h.length,F=[...new Array(y).keys()],N=y<2?"u32":y<=4?`vec${y}<u32>`:`array<u32, ${y}>`,Y=sr(u,B),te=typeof Y=="string"?Y:Y[1],Q=typeof Y=="string"?Y:Y[0],ie={indices:N,value:te,storage:Q,tensor:u},oe=wA=>typeof wA=="string"?wA:`${wA}u`,de={offsetToIndices:!1,indicesToOffset:!1,broadcastedIndicesToOffset:!1,set:!1,setByIndices:!1,get:!1,getByIndices:!1},fe=v?"uniforms.":"",ke=`${fe}${n}_shape`,Me=`${fe}${n}_strides`,De="";for(let wA=0;wA<y-1;wA++)De+=`
    let dim${wA} = current / ${rt(Me,wA,y)};
    let rest${wA} = current % ${rt(Me,wA,y)};
    indices[${wA}] = dim${wA};
    current = rest${wA};
    `;De+=`indices[${y-1}] = current;`;let Le=y<2?"":`
  fn o2i_${n}(offset: u32) -> ${ie.indices} {
    var indices: ${ie.indices};
    var current = offset;
    ${De}
    return indices;
  }`,Xe=[];if(y>=2)for(let wA=y-1;wA>=0;wA--)Xe.push(`${rt(Me,wA,y)} * (indices[${wA}])`);let _e=y<2?"":`
  fn i2o_${n}(indices: ${ie.indices}) -> u32 {
    return ${Xe.join("+")};
  }`,lA=(...wA)=>y===0?"0u":`${ie.indices}(${wA.map(oe).join(",")})`,LA=(wA,UA)=>y<2?`${wA}`:`${rt(wA,UA,y)}`,st={},ht=(wA,UA)=>(()=>{if(ie.storage===ie.value)return`${n}[${wA}]=${UA};`;if(ie.storage==="vec2<u32>"&&ie.value==="i32")return`${n}[${wA}]=vec2<u32>(u32(${UA}), select(0u, 0xFFFFFFFFu, ${UA} < 0));`;if(ie.storage==="vec2<u32>"&&ie.value==="u32")return`${n}[${wA}]=vec2<u32>(u32(${UA}), 0u);`;if(ie.storage==="u32"&&ie.value==="vec4<bool>")return`${n}[${wA}]=dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(${UA}));`;throw new Error(`not supported combination of storage type ${ie.storage} and value type ${ie.value} yet`)})(),pt=wA=>(()=>{if(ie.storage===ie.value)return`${n}[${wA}]`;if(ie.storage==="vec2<u32>"&&ie.value==="i32")return`i32(${n}[${wA}].x)`;if(ie.storage==="vec2<u32>"&&ie.value==="u32")return`u32(${n}[${wA}].x)`;if(ie.storage==="u32"&&ie.value==="vec4<bool>")return`vec4<bool>(bool(${n}[${wA}] & 0xFFu), bool(${n}[${wA}] & 0xFF00u), bool(${n}[${wA}] & 0xFF0000u), bool(${n}[${wA}] & 0xFF000000u))`;throw new Error(`not supported combination of storage type ${ie.storage} and value type ${ie.value} yet`)})(),mt=y<2?"":`
  fn get_${n}ByIndices(indices: ${ie.indices}) -> ${te} {
    return ${pt(`i2o_${n}(indices)`)};
  }`,vt=y<2?"":(()=>{let wA=F.map(At=>`d${At}: u32`).join(", "),UA=F.map(At=>`d${At}`).join(", ");return`
  fn get_${n}(${wA}) -> ${te} {
    return get_${n}ByIndices(${lA(UA)});
  }`})(),tt=y<2?"":`
  fn set_${n}ByIndices(indices: ${ie.indices}, value: ${te}) {
    ${ht(`i2o_${n}(indices)`,"value")}
  }`,lt=y<2?"":(()=>{let wA=F.map(At=>`d${At}: u32`).join(", "),UA=F.map(At=>`d${At}`).join(", ");return`
  fn set_${n}(${wA}, value: ${te}) {
    set_${n}ByIndices(${lA(UA)}, value);
  }`})();return{impl:()=>{let wA=[],UA=!1;return de.offsetToIndices&&(wA.push(Le),UA=!0),de.indicesToOffset&&(wA.push(_e),UA=!0),de.broadcastedIndicesToOffset&&(Object.values(st).forEach(At=>wA.push(At)),UA=!0),de.set&&(wA.push(lt),UA=!0),de.setByIndices&&(wA.push(tt),UA=!0),de.get&&(wA.push(vt),UA=!0),de.getByIndices&&(wA.push(mt),UA=!0),!v&&UA&&wA.unshift(`const ${ke} = ${ie.indices}(${h.join(",")});`,`const ${Me} = ${ie.indices}(${He.computeStrides(h).join(",")});`),wA.join(`
`)},type:ie,offsetToIndices:wA=>(de.offsetToIndices=!0,y<2?wA:`o2i_${n}(${wA})`),indicesToOffset:wA=>(de.indicesToOffset=!0,y<2?wA:`i2o_${n}(${wA})`),broadcastedIndicesToOffset:(wA,UA)=>{de.broadcastedIndicesToOffset=!0;let At=`${UA.name}broadcastedIndicesTo${n}Offset`;if(At in st)return`${At}(${wA})`;let yt=[];for(let gr=y-1;gr>=0;gr--){let la=UA.indicesGet("outputIndices",gr+UA.rank-y);yt.push(`${LA(Me,gr)} * (${la} % ${LA(ke,gr)})`)}return st[At]=`fn ${At}(outputIndices: ${UA.type.indices}) -> u32 {
             return ${yt.length>0?yt.join("+"):"0u"};
           }`,`${At}(${wA})`},indices:lA,indicesGet:LA,indicesSet:(wA,UA,At)=>y<2?`${wA}=${At};`:`${rt(wA,UA,y)}=${At};`,set:(...wA)=>{if(wA.length!==y+1)throw new Error(`indices length must be ${y}`);let UA=wA[y];if(typeof UA!="string")throw new Error("value must be string");let At=wA.slice(0,y).map(oe).join(",");return y===0?ht("0u",UA):y===1?ht(At[0],UA):(de.set=!0,de.setByIndices=!0,de.indicesToOffset=!0,`set_${n}(${At}, ${UA})`)},setByOffset:ht,setByIndices:(wA,UA)=>y<2?ht(wA,UA):(de.setByIndices=!0,de.indicesToOffset=!0,`set_${n}ByIndices(${wA}, ${UA});`),get:(...wA)=>{if(wA.length!==y)throw new Error(`indices length must be ${y}`);let UA=wA.map(oe).join(",");return y===0?pt("0u"):y===1?pt(UA[0]):(de.get=!0,de.getByIndices=!0,de.indicesToOffset=!0,`get_${n}(${UA})`)},getByOffset:pt,getByIndices:wA=>y<2?pt(wA):(de.getByIndices=!0,de.indicesToOffset=!0,`get_${n}ByIndices(${wA})`),usage:w,name:n,strides:Me,shape:ke,rank:y}},gA=(n,u,h,w=1)=>dr(n,u,h,"input",w),JA=(n,u,h,w=1)=>dr(n,u,h,"output",w),pa=(n,u,h)=>dr(n,u,h,"atomicOutput",1),Nr=(n,u,h,w=1)=>dr(n,u,h,"internal",w),Rr=class{constructor(n,u){this.normalizedDispatchGroup=n,this.limits=u,this.internalVariables=[],this.variables=[],this.uniforms=[],this.variableIndex=0}guardAgainstOutOfBoundsWorkgroupSizes(n){return`if (global_idx >= ${typeof n=="number"?`${n}u`:n}) { return; }`}mainStart(n=cr){let u=typeof n=="number"?n:n[0],h=typeof n=="number"?1:n[1],w=typeof n=="number"?1:n[2];if(u>this.limits.maxComputeWorkgroupSizeX||h>this.limits.maxComputeWorkgroupSizeY||w>this.limits.maxComputeWorkgroupSizeZ)throw new Error(`workgroup size [${u}, ${h}, ${w}] exceeds the maximum workgroup size [${this.limits.maxComputeWorkgroupSizeX}, ${this.limits.maxComputeWorkgroupSizeY}, ${this.limits.maxComputeWorkgroupSizeZ}].`);if(u*h*w>this.limits.maxComputeInvocationsPerWorkgroup)throw new Error(`workgroup size [${u}, ${h}, ${w}] exceeds the maximum workgroup invocations ${this.limits.maxComputeInvocationsPerWorkgroup}.`);let B=this.normalizedDispatchGroup[1]===1&&this.normalizedDispatchGroup[2]===1;return`@compute @workgroup_size(${u}, ${h}, ${w})
  fn main(${B?`@builtin(global_invocation_id) global_id : vec3<u32>,
    @builtin(workgroup_id) workgroup_id : vec3<u32>,
    @builtin(local_invocation_index) local_idx : u32,
    @builtin(local_invocation_id) local_id : vec3<u32>`:`@builtin(global_invocation_id) global_id : vec3<u32>,
                                             @builtin(local_invocation_id) local_id : vec3<u32>,
    @builtin(local_invocation_index) local_idx : u32,
    @builtin(workgroup_id) workgroup_id : vec3<u32>,
    @builtin(num_workgroups) num_workgroups : vec3<u32>`}) {
    ${B?`let global_idx = global_id.x;
         let workgroup_index = workgroup_id.x;`:`let workgroup_index = workgroup_id.z * num_workgroups[0] * num_workgroups[1] +
             workgroup_id.y * num_workgroups[0] + workgroup_id.x;
         let global_idx = workgroup_index * ${u*h*w}u + local_idx;`}
  `}appendVariableUniforms(n){n.rank!==0&&(n.shape.startsWith("uniforms.")&&this.uniforms.push({name:n.shape.replace("uniforms.",""),type:"u32",length:n.rank}),n.strides.startsWith("uniforms.")&&this.uniforms.push({name:n.strides.replace("uniforms.",""),type:"u32",length:n.rank}))}declareVariable(n,u){if(n.usage==="internal")throw new Error("cannot use internal variable with declareVariable(). use registerInternalVariables() instead.");this.variables.push(n),this.appendVariableUniforms(n);let h=n.usage==="input"?"read":"read_write",w=n.usage==="atomicOutput"?"atomic<i32>":n.type.storage;return`@group(0) @binding(${u}) var<storage, ${h}> ${n.name}: array<${w}>;`}declareVariables(...n){return n.map(u=>this.declareVariable(u,this.variableIndex++)).join(`
`)}registerInternalVariable(n){if(n.usage!=="internal")throw new Error("cannot use input or output variable with registerInternalVariable(). use declareVariables() instead.");this.internalVariables.push(n),this.appendVariableUniforms(n)}registerInternalVariables(...n){return n.forEach(u=>this.registerInternalVariable(u)),this}registerUniform(n,u,h=1){return this.uniforms.push({name:n,type:u,length:h}),this}registerUniforms(n){return this.uniforms=this.uniforms.concat(n),this}uniformDeclaration(){if(this.uniforms.length===0)return"";let n=[];for(let{name:u,type:h,length:w}of this.uniforms)if(w&&w>4)h==="f16"?n.push(`@align(16) ${u}:array<mat2x4<${h}>, ${Math.ceil(w/8)}>`):n.push(`${u}:array<vec4<${h}>, ${Math.ceil(w/4)}>`);else{let B=w==null||w===1?h:`vec${w}<${h}>`;n.push(`${u}:${B}`)}return`
      struct Uniforms { ${n.join(", ")} };
      @group(0) @binding(${this.variableIndex}) var<uniform> uniforms: Uniforms;`}get additionalImplementations(){return this.uniformDeclaration()+this.variables.map(n=>n.impl()).join(`
`)+this.internalVariables.map(n=>n.impl()).join(`
`)}get variablesInfo(){if(this.uniforms.length===0)return;let n=u=>[12,10,1,6][["u32","f16","f32","i32"].indexOf(u)];return this.uniforms.map(u=>[n(u.type),u.length??1])}},Pr=(n,u)=>new Rr(n,u)}),tn=Pe(()=>{Dt(),St(),qr(),Rt(),Cr=(n,u)=>{if(!n||n.length!==1)throw new Error("Transpose requires 1 input.");if(u.length!==0&&u.length!==n[0].dims.length)throw new Error(`perm size ${u.length} does not match input rank ${n[0].dims.length}`)},Ut=(n,u)=>u.length!==0?u:[...new Array(n).keys()].reverse(),xr=(n,u)=>He.sortBasedOnPerm(n,Ut(n.length,u)),ti=(n,u,h,w)=>{let B=`fn perm(i: ${w.type.indices}) -> ${h.type.indices} {
    var a: ${h.type.indices};`;for(let v=0;v<u;++v)B+=`a[${n[v]}]=i[${v}];`;return B+"return a;}"},Ya=(n,u)=>{let h=[],w=[];for(let B=0;B<n.length;++B)n[B]!==1&&h.push(n[B]),n[u[B]]!==1&&w.push(u[B]);return{newShape:h,newPerm:w}},Fi=(n,u)=>{let h=0;for(let w=0;w<n.length;++w)if(u[n[w]]!==1){if(n[w]<h)return!1;h=n[w]}return!0},Ma=(n,u)=>{let h,w=n.dataType,B=n.dims.length,v=Ut(B,u),y=xr(n.dims,v),F=n.dims,N=y;if(B<2||Fi(v,n.dims))return h=oe=>{let de=gA("input",w,F,4),fe=JA("output",w,N,4);return`
  ${oe.registerUniform("output_size","u32").declareVariables(de,fe)}
  ${oe.mainStart()}
    ${oe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    output[global_idx] = input[global_idx];
  }`},{name:"TransposeCopy",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let oe=He.size(y);return{outputs:[{dims:y,dataType:n.dataType}],dispatchGroup:{x:Math.ceil(oe/64/4)},programUniforms:[{type:12,data:Math.ceil(oe/4)}]}},getShaderSource:h};let{newShape:Y,newPerm:te}=Ya(n.dims,v),Q=He.areEqual(te,[2,3,1]),ie=He.areEqual(te,[3,1,2]);if(Y.length===2||Q||ie){F=Q?[Y[0],Y[1]*Y[2]]:ie?[Y[0]*Y[1],Y[2]]:Y,N=[F[1],F[0]];let oe=16;return h=de=>{let fe=gA("a",w,F.length),ke=JA("output",w,N.length);return`
  ${de.registerUniform("output_size","u32").declareVariables(fe,ke)}
  var<workgroup> tile : array<array<${ke.type.value}, ${oe+1}>, ${oe}>;
  ${de.mainStart([oe,oe,1])}
    let stride = (uniforms.output_shape[1] - 1) / ${oe} + 1;
    let workgroup_id_x = workgroup_index % stride;
    let workgroup_id_y = workgroup_index / stride;
    let input_col = workgroup_id_y * ${oe}u + local_id.x;
    let input_row = workgroup_id_x * ${oe}u + local_id.y;
    if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) {
      tile[local_id.y][local_id.x] = ${fe.getByIndices(`${fe.type.indices}(input_row, input_col)`)};
    }
    workgroupBarrier();

    let output_col = workgroup_id_x * ${oe}u + local_id.x;
    let output_row = workgroup_id_y * ${oe}u + local_id.y;
    if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) {
      ${ke.setByIndices(`${ke.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")}
    }
  }`},{name:"TransposeShared",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let de=He.size(y);return{outputs:[{dims:y,dataType:n.dataType}],dispatchGroup:{x:Math.ceil(N[1]/oe),y:Math.ceil(N[0]/oe)},programUniforms:[{type:12,data:de},...KA(F,N)]}},getShaderSource:h}}return h=oe=>{let de=gA("a",w,F.length),fe=JA("output",w,N.length);return`
  ${oe.registerUniform("output_size","u32").declareVariables(de,fe)}

  ${ti(v,B,de,fe)}

  ${oe.mainStart()}
    ${oe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

    let indices = ${fe.offsetToIndices("global_idx")};
    let aIndices = perm(indices);

    ${fe.setByOffset("global_idx",de.getByIndices("aIndices"))}
  }`},{name:"Transpose",shaderCache:{hint:`${u}`,inputDependencies:["rank"]},getRunData:()=>{let oe=He.size(y);return{outputs:[{dims:y,dataType:n.dataType}],dispatchGroup:{x:Math.ceil(oe/64)},programUniforms:[{type:12,data:oe},...KA(F,N)]}},getShaderSource:h}},Si=(n,u)=>{Cr(n.inputs,u.perm),n.compute(Ma(n.inputs[0],u.perm))},Gt=n=>$e({perm:n.perm})}),lc=Pe(()=>{Dt(),St(),Rt(),i1(),tn(),ea={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate * candidate",logSumExp:"bestValue + exp(candidate)",l1:"bestValue + abs(candidate)",l2:"bestValue + candidate * candidate",logSum:"bestValue + candidate"},Wa={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate",logSumExp:"bestValue + candidate",l1:"bestValue + candidate",l2:"bestValue + candidate",logSum:"bestValue + candidate"},s0={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},i0={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},n0=(n,u)=>{let h=[];for(let w=u-n;w<u;++w)h.push(w);return h},o0=(n,u)=>{let h=[],w=n.length;for(let v=0;v<w;v++)u.indexOf(v)===-1&&h.push(n[v]);let B=u.map(v=>n[v]);return[h,B]},ri=(n,u)=>{let h=n.length+u.length,w=[],B=0;for(let v=0;v<h;v++)u.indexOf(v)===-1?w.push(n[B++]):w.push(1);return w},Rs=(n,u)=>{for(let h=0;h<n.length;++h)if(n[n.length-h-1]!==u-1-h)return!1;return!0},l0=(n,u)=>{let h=[];if(!Rs(n,u)){for(let w=0;w<u;++w)n.indexOf(w)===-1&&h.push(w);n.forEach(w=>h.push(w))}return h},Ni=(n,u,h,w,B,v,y)=>{let F=h[0].dims,N=He.size(v),Y=He.size(y),te=gA("_A",h[0].dataType,F),Q=JA("output",B,v),ie=64;N===1&&(ie=256);let oe=`
          var<workgroup> aBestValues : array<f32, ${ie}>;
       `;return{name:n,shaderCache:{hint:`${u};${ie}`,inputDependencies:["type"]},getShaderSource:de=>`
        ${de.registerUniform("reduceSize","u32").declareVariables(te,Q)}
        ${oe}
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
          return ((a - 1u) / b + 1u);
         }
         ${de.mainStart(ie)}

          let outputIndex = global_idx / ${ie};
          let offset = outputIndex * uniforms.reduceSize;

          var bestValue = f32(${s0[w]});
          let Length = uniforms.reduceSize;
          for (var k = local_idx; k < Length; k = k + ${ie}) {
           let candidate = f32(${te.getByOffset("offset + k")});
           bestValue = ${ea[w]};
          }
          aBestValues[local_idx] = bestValue;
          workgroupBarrier();

         var reduceSize = min(Length, ${ie}u);
         for (var currentSize = reduceSize / 2u; reduceSize > 1u;
             currentSize = reduceSize / 2u) {
           let interval = DIV_CEIL(reduceSize, 2u);
           if (local_idx < currentSize) {
            let candidate = aBestValues[local_idx + interval];
            bestValue = ${Wa[w]};
            aBestValues[local_idx] = bestValue;
           }
           reduceSize = interval;
           workgroupBarrier();
         }

         if (local_idx == 0u) {
          ${Q.setByOffset("outputIndex",w==="mean"?`${Q.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${Q.type.storage}(${i0[w]})`)};
         }
        }`,getRunData:()=>({outputs:[{dims:v,dataType:B}],dispatchGroup:{x:N},programUniforms:[{type:12,data:Y}]})}},Qa=(n,u,h,w)=>{let B=n.inputs.length===1?h:Ii(n.inputs,h),v=B.axes;v.length===0&&!B.noopWithEmptyAxes&&(v=n.inputs[0].dims.map((oe,de)=>de));let y=He.normalizeAxes(v,n.inputs[0].dims.length),F=y,N=n.inputs[0],Y=l0(F,n.inputs[0].dims.length);Y.length>0&&(N=n.compute(Ma(n.inputs[0],Y),{inputs:[0],outputs:[-1]})[0],F=n0(F.length,N.dims.length));let[te,Q]=o0(N.dims,F),ie=te;B.keepDims&&(ie=ri(te,y)),n.compute(Ni(u,B.cacheKey,[N],w,n.inputs[0].dataType,ie,Q),{inputs:[N]})},Kr=(n,u)=>{Qa(n,"ReduceMeanShared",u,"mean")},qA=(n,u)=>{Qa(n,"ReduceL1Shared",u,"l1")},Ga=(n,u)=>{Qa(n,"ReduceL2Shared",u,"l2")},dn=(n,u)=>{Qa(n,"ReduceLogSumExpShared",u,"logSumExp")},Oi=(n,u)=>{Qa(n,"ReduceMaxShared",u,"max")},fn=(n,u)=>{Qa(n,"ReduceMinShared",u,"min")},c0=(n,u)=>{Qa(n,"ReduceProdShared",u,"prod")},g0=(n,u)=>{Qa(n,"ReduceSumShared",u,"sum")},pn=(n,u)=>{Qa(n,"ReduceSumSquareShared",u,"sumSquare")},j0=(n,u)=>{Qa(n,"ReduceLogSumShared",u,"logSum")}}),i1=Pe(()=>{Dt(),St(),qr(),Rt(),lc(),Ta=n=>{if(!n||n.length===0||n.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(n.length===2&&n[1].dims.length!==1)throw new Error("Invalid axes input dims.")},rr=n=>["","",`var value = ${n.getByIndices("input_indices")};`,""],bi=(n,u,h,w,B,v,y=!1,F=!1)=>{let N=[],Y=h[0].dims,te=Y.length,Q=He.normalizeAxes(B,te),ie=!F&&Q.length===0;Y.forEach((fe,ke)=>{ie||Q.indexOf(ke)>=0?y&&N.push(1):N.push(fe)});let oe=N.length,de=He.size(N);return{name:n,shaderCache:u,getShaderSource:fe=>{let ke=[],Me=gA("_A",h[0].dataType,te),De=JA("output",v,oe),Le=w(Me,De,Q),Xe=Le[2];for(let _e=0,lA=0;_e<te;_e++)ie||Q.indexOf(_e)>=0?(y&&lA++,Xe=`for(var j${_e}: u32 = 0; j${_e} < ${Y[_e]}; j${_e}++) {
                  ${Le[2].includes("last_index")?`let last_index = j${_e};`:""}
                  ${Me.indicesSet("input_indices",_e,`j${_e}`)}
                  ${Xe}
                }`):(ke.push(`${Me.indicesSet("input_indices",_e,De.indicesGet("output_indices",lA))};`),lA++);return`

        ${fe.registerUniform("output_size","u32").declareVariables(Me,De)}

        ${fe.mainStart()}
          ${fe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          var input_indices: ${Me.type.indices};
          let output_indices = ${De.offsetToIndices("global_idx")};

          ${ke.join(`
`)}
          ${Le[0]}       // init ops for reduce max/min
          ${Le[1]}
          ${Xe}
          ${Le[3]}
          ${Le.length===4?De.setByOffset("global_idx","value"):Le.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:N,dataType:v}],dispatchGroup:{x:Math.ceil(de/64)},programUniforms:[{type:12,data:de},...KA(Y,N)]})}},Ii=(n,u)=>{let h=[];return n[1].dims[0]>0&&n[1].getBigInt64Array().forEach(w=>h.push(Number(w))),$e({axes:h,keepDims:u.keepDims,noopWithEmptyAxes:u.noopWithEmptyAxes})},ka=(n,u,h,w)=>{let B=n.inputs,v=B.length===1?h:Ii(B,h);n.compute(bi(u,{hint:v.cacheKey,inputDependencies:["rank"]},[B[0]],v.noopWithEmptyAxes&&v.axes.length===0?rr:w,v.axes,B[0].dataType,v.keepDims,v.noopWithEmptyAxes),{inputs:[0]})},Ws=(n,u)=>{Ta(n.inputs),ka(n,"ReduceLogSum",u,(h,w)=>[`var value = ${w.type.storage}(0);`,"",`value += ${h.getByIndices("input_indices")};`,"value = log(value);"])},as=(n,u)=>{Ta(n.inputs),ka(n,"ReduceL1",u,(h,w)=>[`var value = ${w.type.storage}(0);`,"",`value += abs(${h.getByIndices("input_indices")});`,""])},hn=(n,u)=>{Ta(n.inputs),ka(n,"ReduceL2",u,(h,w)=>[`var t = ${w.type.value}(0); var value = ${w.type.value}(0);`,"",`t = ${h.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])},mn=(n,u)=>{Ta(n.inputs),ka(n,"ReduceLogSumExp",u,(h,w)=>[`var value = ${w.type.storage}(0);`,"",`value += exp(${h.getByIndices("input_indices")});`,"value = log(value);"])},Vs=(n,u)=>{Ta(n.inputs),ka(n,"ReduceMax",u,(h,w,B)=>{let v=[];for(let y=0;y<h.rank;y++)(B.indexOf(y)>=0||B.length===0)&&v.push(h.indicesSet("input_indices",y,0));return[`${v.join(`
`)}`,`var value = ${h.getByIndices("input_indices")};`,`value = max(value, ${h.getByIndices("input_indices")});`,""]})},Cn=(n,u)=>{Ta(n.inputs),ka(n,"ReduceMean",u,(h,w,B)=>{let v=1;for(let y=0;y<h.rank;y++)(B.indexOf(y)>=0||B.length===0)&&(v*=n.inputs[0].dims[y]);return["var sum = f32(0);","",`sum += f32(${h.getByIndices("input_indices")});`,`let value = ${w.type.value}(sum / ${v});`]})},bn=(n,u)=>{Ta(n.inputs),ka(n,"ReduceMin",u,(h,w,B)=>{let v=[];for(let y=0;y<h.rank;y++)(B.indexOf(y)>=0||B.length===0)&&v.push(`input_indices[${y}] = 0;`);return[`${v.join(`
`)}`,`var value = ${h.getByIndices("input_indices")};`,`value = min(value, ${h.getByIndices("input_indices")});`,""]})},In=(n,u)=>{Ta(n.inputs),ka(n,"ReduceProd",u,(h,w)=>[`var value = ${w.type.storage}(1);`,"",`value *= ${h.getByIndices("input_indices")};`,""])},DA=(n,u)=>{Ta(n.inputs),ka(n,"ReduceSum",u,(h,w)=>[`var value = ${w.type.storage}(0);`,"",`value += ${h.getByIndices("input_indices")};`,""])},js=(n,u)=>{Ta(n.inputs),ka(n,"ReduceSumSquare",u,(h,w)=>[`var t = ${w.type.value}(0); var value = ${w.type.value}(0);`,"",`t = ${h.getByIndices("input_indices")}; value += t * t;`,""])},RA=(n,u,h)=>{if(u.length===0)return h;let w=1,B=1;for(let v=0;v<u.length;v++)u.indexOf(v)===-1?w*=n[v]:B*=n[v];return B<32&&w>1024},wn=(n,u)=>{RA(n.inputs[0].dims,u.axes,u.noopWithEmptyAxes)?Cn(n,u):Kr(n,u)},Li=(n,u)=>{RA(n.inputs[0].dims,u.axes,u.noopWithEmptyAxes)?as(n,u):qA(n,u)},xa=(n,u)=>{RA(n.inputs[0].dims,u.axes,u.noopWithEmptyAxes)?hn(n,u):Ga(n,u)},Pa=(n,u)=>{RA(n.inputs[0].dims,u.axes,u.noopWithEmptyAxes)?mn(n,u):dn(n,u)},ai=(n,u)=>{RA(n.inputs[0].dims,u.axes,u.noopWithEmptyAxes)?Vs(n,u):Oi(n,u)},wi=(n,u)=>{RA(n.inputs[0].dims,u.axes,u.noopWithEmptyAxes)?bn(n,u):fn(n,u)},kn=(n,u)=>{RA(n.inputs[0].dims,u.axes,u.noopWithEmptyAxes)?In(n,u):c0(n,u)},zr=(n,u)=>{RA(n.inputs[0].dims,u.axes,u.noopWithEmptyAxes)?DA(n,u):g0(n,u)},u0=(n,u)=>{RA(n.inputs[0].dims,u.axes,u.noopWithEmptyAxes)?js(n,u):pn(n,u)},d0=(n,u)=>{RA(n.inputs[0].dims,u.axes,u.noopWithEmptyAxes)?Ws(n,u):j0(n,u)}}),cc=Pe(()=>{Dt(),qr(),i1(),Es=n=>{if(!n||n.length===0||n.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(n[0].dataType!==1)throw new Error("Invalid input type.")},si=(n,u)=>{Es(n.inputs),n.compute(bi("ArgMin",{hint:u.cacheKey,inputDependencies:["rank"]},[n.inputs[0]],(h,w,B)=>{let v=[];for(let y=0;y<h.rank;y++)(B.indexOf(y)>=0||B.length===0)&&v.push(`input_indices[${y}] = 0;`);return[`${v.join(`
`)}`,`var value = ${h.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${h.getByIndices("input_indices")} ${u.selectLastIndex>0?"<=":"<"} value) {
         value = ${h.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",w.setByOffset("global_idx","best_index")]},[u.axis],7,u.keepDims),{inputs:[0]})},f0=(n,u)=>{Es(n.inputs),n.compute(bi("argMax",{hint:u.cacheKey,inputDependencies:["rank"]},[n.inputs[0]],(h,w,B)=>{let v=[];for(let y=0;y<h.rank;y++)(B.indexOf(y)>=0||B.length===0)&&v.push(`input_indices[${y}] = 0;`);return[`${v.join(`
`)}`,`var value = ${h.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${h.getByIndices("input_indices")} ${u.selectLastIndex>0?">=":">"} value) {
         value = ${h.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",w.setByOffset("global_idx","best_index")]},[u.axis],7,u.keepDims),{inputs:[0]})},Yi=n=>$e(n)}),n1=Pe(()=>{Dt(),St(),s1(),Rt(),ii=(n,u)=>{let h=n[0],w=n[1],B=n[2],v=n[3],y=n[4],F=n[5];if(y&&F)throw new Error("Attention cannot have both past and attention_bias");if(h.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let N=h.dims[0],Y=h.dims[1],te=h.dims[2];if(B.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(w.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(w.dims[0]!==te)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(B.dims[0]!==w.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let Q=B.dims[0]/3,ie=Q,oe=ie;if(u.qkvHiddenSizes.length>0){if(u.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let Me of u.qkvHiddenSizes)if(Me%u.numHeads!=0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");Q=u.qkvHiddenSizes[0],ie=u.qkvHiddenSizes[1],oe=u.qkvHiddenSizes[2]}let de=Y;if(Q!==ie)throw new Error("qkv_hidden_sizes first element should be same as the second");if(B.dims[0]!==Q+ie+oe)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let fe=0;if(y){if(ie!==oe)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(y.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(y.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(y.dims[1]!==N)throw new Error('Input "past" second dimension must be batch_size');if(y.dims[2]!==u.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(y.dims[4]!==ie/u.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');u.pastPresentShareBuffer||(fe=y.dims[3])}let ke=de+fe;if(v)throw new Error("Mask not supported");if(y)throw new Error("past is not supported");if(F){if(F.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(F.dims[0]!==N||F.dims[1]!==u.numHeads||F.dims[2]!==Y||F.dims[3]!==ke)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:N,sequenceLength:Y,pastSequenceLength:fe,kvSequenceLength:de,totalSequenceLength:ke,maxSequenceLength:-1,inputHiddenSize:te,hiddenSize:Q,vHiddenSize:oe,headSize:Math.floor(Q/u.numHeads),vHeadSize:Math.floor(oe/u.numHeads),numHeads:u.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:u.maskFilterValue,maskType:0,scale:u.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},HA=(n,u,h)=>u&&n?`
      let total_sequence_length_input = u32(${u.getByOffset("0")});
      let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length);
      let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input;
      let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input;
      total_sequence_length = u32(${n?.getByOffset("batchIdx")}) + 1;
      var past_sequence_length: u32 = 0;
      if (is_first_prompt == false) {
        past_sequence_length = total_sequence_length - sequence_length;
      }
       `:`
    ${h?"let past_sequence_length = uniforms.past_sequence_length":""};
    let present_sequence_length = total_sequence_length;
    `,Ja=(n,u,h,w,B,v,y,F)=>{let N=Jt(y?1:v),Y=64,te=v/N;te<Y&&(Y=32);let Q=Math.ceil(v/N/Y),ie=[{type:12,data:u},{type:12,data:h},{type:12,data:w},{type:12,data:B},{type:12,data:te},{type:12,data:Q}],oe=jt(n.dataType,N),de=jA(1,N),fe=["type"];return y&&fe.push("type"),F&&fe.push("type"),{name:"AttentionProbsSoftmax",shaderCache:{hint:`${Y};${oe};${N}`,inputDependencies:fe},getShaderSource:ke=>{let Me=JA("x",n.dataType,n.dims,N),De=[Me],Le=y?gA("seq_lens",y.dataType,y.dims):void 0;Le&&De.push(Le);let Xe=F?gA("total_sequence_length_input",F.dataType,F.dims):void 0;Xe&&De.push(Xe);let _e=jA(n.dataType);return`
  var<workgroup> thread_max: array<f32, ${Y}>;
  var<workgroup> thread_sum: array<f32, ${Y}>;
  ${ke.registerUniforms([{name:"batch_size",type:"u32"},{name:"num_heads",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"sequence_length",type:"u32"},{name:"total_sequence_length",type:"u32"},{name:"elements_per_thread",type:"u32"}]).declareVariables(...De)}
  ${ke.mainStart([Y,1,1])}
    let batchIdx = workgroup_id.z / uniforms.num_heads;
    let headIdx = workgroup_id.z % uniforms.num_heads;
    let sequence_length = uniforms.sequence_length;
    var total_sequence_length = uniforms.total_sequence_length;
    ${HA(Le,Xe,!1)}
    let local_offset = local_idx * uniforms.elements_per_thread;
    let offset = (global_idx / ${Y}) * uniforms.total_sequence_length + local_offset;
    let seq_causal_length = ${y?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"};
    var thread_max_vector = ${de}(-3.402823e+38f);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
      thread_max_vector = max(${de}(x[offset + i]), thread_max_vector);
    }
    thread_max[local_idx] = ${(()=>{switch(N){case 1:return"thread_max_vector";case 2:return"max(thread_max_vector.x, thread_max_vector.y)";case 4:return"max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))";default:throw new Error(`Unsupported components: ${N}`)}})()};
    workgroupBarrier();

    var max_value =  f32(-3.402823e+38f);
    for (var i = 0u; i < ${Y}; i++) {
      max_value = max(thread_max[i], max_value);
    }

    var sum_vector = ${de}(0);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
      sum_vector += exp(${de}(x[offset + i]) - max_value);
    }
    thread_sum[local_idx] = ${(()=>{switch(N){case 1:return"sum_vector";case 2:return"sum_vector.x + sum_vector.y";case 4:return"sum_vector.x + sum_vector.y + sum_vector.z + sum_vector.w";default:throw new Error(`Unsupported components: ${N}`)}})()};
    workgroupBarrier();

    var sum: f32 = 0;
    for (var i = 0u; i < ${Y}; i++) {
      sum += thread_sum[i];
    }

    if (sum == 0) {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
        x[offset + i] = ${Me.type.value}(${_e}(1.0) / ${_e}(seq_causal_length));
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
        var f32input = ${de}(x[offset + i]);
        x[offset + i] = ${Me.type.value}(exp(f32input - max_value) / sum);
      }
    }
      ${y?`
        for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) {
          x[offset + total_seq_id] = ${Me.type.value}(${_e}(0));
        }`:""};
  }`},getRunData:()=>({outputs:[],dispatchGroup:{x:Math.ceil(v/Y),y:B,z:u*h},programUniforms:ie})}},xn=(n,u,h,w,B,v,y,F,N)=>{let Y=y+v.kvSequenceLength,te=[v.batchSize,v.numHeads,v.sequenceLength,Y],Q=n>1&&w,ie=v.kvNumHeads?v.kvNumHeads:v.numHeads,oe=Q?[v.batchSize,ie,Y,v.headSize]:void 0,de=v.nReps?v.nReps:1,fe=v.scale===0?1/Math.sqrt(v.headSize):v.scale,ke=Jt(v.headSize),Me=v.headSize/ke,De=12,Le={x:Math.ceil(Y/De),y:Math.ceil(v.sequenceLength/De),z:v.batchSize*v.numHeads},Xe=[{type:12,data:v.sequenceLength},{type:12,data:Me},{type:12,data:Y},{type:12,data:v.numHeads},{type:12,data:v.headSize},{type:1,data:fe},{type:12,data:y},{type:12,data:v.kvSequenceLength},{type:12,data:de}],_e=Q&&w&&He.size(w.dims)>0,lA=["type","type"];_e&&lA.push("type"),B&&lA.push("type"),F&&lA.push("type"),N&&lA.push("type");let LA=[{dims:te,dataType:u.dataType,gpuDataType:0}];return Q&&LA.push({dims:oe,dataType:u.dataType,gpuDataType:0}),{name:"AttentionProbs",shaderCache:{hint:`${ke};${B!==void 0};${w!==void 0};${n}`,inputDependencies:lA},getRunData:()=>({outputs:LA,dispatchGroup:Le,programUniforms:Xe}),getShaderSource:st=>{let ht=gA("q",u.dataType,u.dims,ke),pt=[ht,gA("key",h.dataType,h.dims,ke)];if(_e){let UA=gA("past_key",w.dataType,w.dims,ke);pt.push(UA)}B&&pt.push(gA("attention_bias",B.dataType,B.dims));let mt=F?gA("seq_lens",F.dataType,F.dims):void 0;mt&&pt.push(mt);let vt=N?gA("total_sequence_length_input",N.dataType,N.dims):void 0;vt&&pt.push(vt);let tt=JA("output",u.dataType,te),lt=[tt];Q&&lt.push(JA("present_key",u.dataType,oe,ke));let wA=jA(1,ke);return`
  const TILE_SIZE = 12u;

  var<workgroup> tileQ: array<${ht.type.storage}, 144>;
  var<workgroup> tileK: array<${ht.type.storage}, 144>;
  ${st.registerUniforms([{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}]).declareVariables(...pt,...lt)}
  ${st.mainStart([De,De,1])}
    // x holds the N and y holds the M
    let headIdx = workgroup_id.z % uniforms.num_heads;
    let kvHeadIdx = ${de===1?"headIdx":"headIdx / uniforms.n_reps"};
    let kv_num_heads = ${de===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
    let batchIdx = workgroup_id.z / uniforms.num_heads;
    let m = workgroup_id.y * TILE_SIZE;
    let n = workgroup_id.x * TILE_SIZE;
    let sequence_length = uniforms.M;
    var total_sequence_length = uniforms.N;
    ${HA(mt,vt,!0)}
    let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx;
    let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
    ${_e&&Q?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""};
    let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K;
    ${Q?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""}
    var value = ${wA}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) {
        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];
      }
      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {
        var idx = TILE_SIZE * local_id.y + local_id.x;
      ${_e&&Q?`
              if (n + local_id.y < past_sequence_length) {
                tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
              } else if (n + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
                tileK[idx] = key[kOffset + (n + local_id.y - past_sequence_length) * uniforms.K + w + local_id.x];
              }`:`
          if (n + local_id.y < uniforms.kv_sequence_length) {
            tileK[idx] = key[kOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
          }`}
      ${Q?`if (n + local_id.y < present_sequence_length) {
        present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx];
      }`:""}
      }
      workgroupBarrier();

      for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {
          value += ${wA}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]);
      }

      workgroupBarrier();
    }

    if (global_id.y < uniforms.M && global_id.x < total_sequence_length) {
      let headOffset = workgroup_id.z * uniforms.M * uniforms.N;
      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
      var sum: f32 = ${(()=>{switch(ke){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${ke}`)}})()};
        output[outputIdx] = ${tt.type.value} (sum * uniforms.alpha) + ${B?"attention_bias[outputIdx]":"0.0"};
    }
  }`}}},ki=(n,u,h,w,B,v,y=void 0,F=void 0)=>{let N=v+B.kvSequenceLength,Y=B.nReps?B.nReps:1,te=B.vHiddenSize*Y,Q=n>1&&w,ie=B.kvNumHeads?B.kvNumHeads:B.numHeads,oe=Q?[B.batchSize,ie,N,B.headSize]:void 0,de=[B.batchSize,B.sequenceLength,te],fe=12,ke={x:Math.ceil(B.vHeadSize/fe),y:Math.ceil(B.sequenceLength/fe),z:B.batchSize*B.numHeads},Me=[{type:12,data:B.sequenceLength},{type:12,data:N},{type:12,data:B.vHeadSize},{type:12,data:B.numHeads},{type:12,data:B.headSize},{type:12,data:te},{type:12,data:v},{type:12,data:B.kvSequenceLength},{type:12,data:Y}],De=Q&&w&&He.size(w.dims)>0,Le=["type","type"];De&&Le.push("type"),y&&Le.push("type"),F&&Le.push("type");let Xe=[{dims:de,dataType:u.dataType,gpuDataType:0}];return Q&&Xe.push({dims:oe,dataType:u.dataType,gpuDataType:0}),{name:"AttentionScore",shaderCache:{hint:`${w!==void 0};${n}`,inputDependencies:Le},getRunData:()=>({outputs:Xe,dispatchGroup:ke,programUniforms:Me}),getShaderSource:_e=>{let lA=gA("probs",u.dataType,u.dims),LA=[lA,gA("v",h.dataType,h.dims)];De&&LA.push(gA("past_value",w.dataType,w.dims));let st=y?gA("seq_lens",y.dataType,y.dims):void 0;y&&LA.push(st);let ht=F?gA("total_sequence_length_input",F.dataType,F.dims):void 0;F&&LA.push(ht);let pt=[JA("output",u.dataType,de)];return Q&&pt.push(JA("present_value",u.dataType,oe)),`
  const TILE_SIZE = 12u;
  var<workgroup> tileQ: array<${lA.type.value}, 144>;
  var<workgroup> tileV: array<${lA.type.value}, 144>;
  ${_e.registerUniforms([{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}]).declareVariables(...LA,...pt)}
  ${_e.mainStart([fe,fe,1])}
   let headIdx = workgroup_id.z % uniforms.num_heads;
   let batchIdx = workgroup_id.z / uniforms.num_heads;
   let kvHeadIdx = ${Y===1?"headIdx":"headIdx / uniforms.n_reps"};
   let kv_num_heads = ${Y===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
   let m = global_id.y;
   let n = global_id.x;
   let sequence_length = uniforms.M;
   var total_sequence_length = uniforms.K;
   ${HA(st,ht,!0)}
   let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
   let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch
   ${De&&Q?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""};
   let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n;
   ${Q?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""}
   var value = ${lA.type.storage}(0);
   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        var idx = TILE_SIZE * local_id.y + local_id.x;
        ${De&&Q?`
        if (w + local_id.y < past_sequence_length) {
          tileV[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N];
        } else if (w + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
          tileV[idx] = v[vOffset + (w + local_id.y - past_sequence_length) * uniforms.N];
        }
      `:`
            if (w + local_id.y < uniforms.kv_sequence_length) {
              tileV[idx] = v[vOffset + (w + local_id.y) * uniforms.N];
            }`}
        ${Q?`
            if (w + local_id.y < present_sequence_length) {
          present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx];
        }`:""}
      }
     workgroupBarrier();
     for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) {
       value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x];
     }
     workgroupBarrier();
   }

   // we need to transpose output from BNSH_v to BSND_v
   if (m < uniforms.M && n < uniforms.N) {
     let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size
       + headIdx * uniforms.N + n;
     output[outputIdx] = value;
   }
  }`}}},fs=(n,u,h,w,B,v,y,F,N,Y,te=void 0,Q=void 0)=>{let ie=Math.min(n.outputCount,1+(y?1:0)+(F?1:0)),oe=ie>1?Y.pastSequenceLength:0,de=oe+Y.kvSequenceLength,fe=N&&He.size(N.dims)>0?N:void 0,ke=[u,h];ie>1&&y&&He.size(y.dims)>0&&ke.push(y),fe&&ke.push(fe),te&&ke.push(te),Q&&ke.push(Q);let Me=n.compute(xn(ie,u,h,y,fe,Y,oe,te,Q),{inputs:ke,outputs:ie>1?[-1,1]:[-1]})[0];n.compute(Ja(Me,Y.batchSize,Y.numHeads,oe,Y.sequenceLength,de,te,Q),{inputs:te&&Q?[Me,te,Q]:[Me],outputs:[]});let De=[Me,w];ie>1&&F&&He.size(F.dims)>0&&De.push(F),te&&De.push(te),Q&&De.push(Q),n.compute(ki(ie,Me,w,F,Y,oe,te,Q),{inputs:De,outputs:ie>1?[0,2]:[0]})},Bn=(n,u)=>{let h=[u.batchSize,u.numHeads,u.sequenceLength,u.headSize],w=u.sequenceLength,B=u.inputHiddenSize,v=u.headSize,y=12,F={x:Math.ceil(u.headSize/y),y:Math.ceil(u.sequenceLength/y),z:u.batchSize*u.numHeads},N=[n.inputs[0],n.inputs[1],n.inputs[2]],Y=[{type:12,data:w},{type:12,data:B},{type:12,data:v},{type:12,data:u.numHeads},{type:12,data:u.headSize},{type:12,data:u.hiddenSize},{type:12,data:u.hiddenSize+u.hiddenSize+u.vHiddenSize}];return n.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:h,dataType:n.inputs[0].dataType,gpuDataType:0},{dims:h,dataType:n.inputs[0].dataType,gpuDataType:0},{dims:h,dataType:n.inputs[0].dataType,gpuDataType:0}],dispatchGroup:F,programUniforms:Y}),getShaderSource:te=>{let Q=JA("output_q",N[0].dataType,h),ie=JA("output_k",N[0].dataType,h),oe=JA("output_v",N[0].dataType,h),de=gA("input",N[0].dataType,N[0].dims),fe=gA("weight",N[1].dataType,N[1].dims),ke=gA("bias",N[2].dataType,N[2].dims),Me=de.type.storage;return`
  const TILE_SIZE = 12u;
  var<workgroup> tileInput: array<${Me}, 144>;
  var<workgroup> tileWeightQ: array<${Me}, 144>;
  var<workgroup> tileWeightK: array<${Me}, 144>;
  var<workgroup> tileWeightV: array<${Me}, 144>;
  ${te.registerUniforms([{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}]).declareVariables(de,fe,ke,Q,ie,oe)}
  ${te.mainStart([y,y,1])}
    let batchIndex = workgroup_id.z / uniforms.num_heads;
    let headNumber = workgroup_id.z % uniforms.num_heads;
    let m = global_id.y;
    let n = global_id.x;

    let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;
    let biasOffsetQ = headNumber * uniforms.head_size;
    let biasOffsetK = uniforms.hidden_size + biasOffsetQ;
    let biasOffsetV = uniforms.hidden_size + biasOffsetK;

    var valueQ = ${Me}(0);
    var valueK = ${Me}(0);
    var valueV = ${Me}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        let offset = n + (w + local_id.y) * uniforms.ldb;
        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
      }
      workgroupBarrier();
      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
        let inputTileOffset = TILE_SIZE * local_id.y + k;
        let weightTileOffset = TILE_SIZE * k + local_id.x;
        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
      }

      workgroupBarrier();
    }

    let headOffset = (m * uniforms.N + n) % uniforms.head_size;
    valueQ += bias[headOffset + biasOffsetQ];
    valueK += bias[headOffset + biasOffsetK];
    valueV += bias[headOffset + biasOffsetV];

    let offset = workgroup_id.z * uniforms.M * uniforms.N;
    if (m < uniforms.M && n < uniforms.N) {
      let outputIdx = offset + m * uniforms.N + n;
      output_q[outputIdx] = valueQ;
      output_k[outputIdx] = valueK;
      output_v[outputIdx] = valueV;
    }
  }`}},{inputs:N,outputs:[-1,-1,-1]})},p0=(n,u)=>{let h=ii(n.inputs,u),[w,B,v]=Bn(n,h);return fs(n,w,B,v,n.inputs[4],void 0,void 0,void 0,n.inputs[5],h)}}),gc=Pe(()=>{dA(),Dt(),St(),qr(),Rt(),h0=(n,u)=>{if(!n||n.length!==5)throw new Error("BatchNormalization requires 5 inputs");let h=(w,B,v)=>{let y=B.length;if(y!==w.length)throw new Error(`${v}: num dimensions != ${y}`);B.forEach((F,N)=>{if(F!==w[N])throw new Error(`${v}: dim[${N}] do not match`)})};if(n[0].dims.length>1){let w=u.format==="NHWC"?u.spatial?n[0].dims.slice(-1):n[0].dims.slice(-1).concat(n[0].dims.slice(1,n[0].dims.length-1)):n[0].dims.slice(1,u.spatial?2:void 0);h(n[1].dims,w,"Invalid input scale"),h(n[2].dims,w,"Invalid input B"),h(n[3].dims,w,"Invalid input mean"),h(n[4].dims,w,"Invalid input var")}else h(n[1].dims,[1],"Invalid input scale"),h(n[2].dims,[1],"Invalid input B"),h(n[3].dims,[1],"Invalid input mean"),h(n[4].dims,[1],"Invalid input var")},Hi=(n,u)=>{let{epsilon:h,spatial:w,format:B}=u,v=n[0].dims,y=w?Jt(v[v.length-1]):1,F=B==="NHWC"&&v.length>1?y:1,N=He.size(v)/y,Y=w,te=Y?v.length:v,Q=gA("x",n[0].dataType,n[0].dims,y),ie=gA("scale",n[1].dataType,n[1].dims,F),oe=gA("bias",n[2].dataType,n[2].dims,F),de=gA("inputMean",n[3].dataType,n[3].dims,F),fe=gA("inputVar",n[4].dataType,n[4].dims,F),ke=JA("y",n[0].dataType,te,y);return{name:"BatchNormalization",shaderCache:{hint:`${u.epsilon}_${u.format}_${w}_${y}`,inputDependencies:Y?["rank","type","type","type","type"]:void 0},getShaderSource:Me=>`
  const epsilon = ${h};
  ${Me.registerUniform("outputSize","u32").declareVariables(Q,ie,oe,de,fe,ke)}
  ${Me.mainStart()}
  ${Me.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${ke.offsetToIndices(`global_idx * ${y}`)};
    ${(()=>{let De="";if(w)De=`let cOffset = ${v.length===1?"0u":B==="NHWC"?`outputIndices[${v.length-1}] / ${y}`:"outputIndices[1]"};`;else if(B==="NCHW")De=`
            ${ke.indicesSet("outputIndices","0","0")}
            let cOffset = ${ke.indicesToOffset("outputIndices")};`;else{De=`var cIndices = ${ie.type.indices}(0);
                       cIndices[0] = outputIndices[${v.length-1}];`;for(let Le=1;Le<ie.rank;Le++)De+=`cIndices[${Le}] = outputIndices[${Le}];`;De+=`let cOffset = ${ie.indicesToOffset("cIndices")};`}return De})()}
    let scale = ${ie.getByOffset("cOffset")};
    let bias = ${oe.getByOffset("cOffset")};
    let inputMean = ${de.getByOffset("cOffset")};
    let inputVar = ${fe.getByOffset("cOffset")};
    let x = ${Q.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${ke.setByOffset("global_idx","value")}
  }`,getRunData:()=>({outputs:[{dims:n[0].dims,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(N/64)},programUniforms:Y?[{type:12,data:N},...KA(v)]:[{type:12,data:N}]})}},m0=n=>$e(n),C0=(n,u)=>{let{inputs:h,outputCount:w}=n,B=m0({...u,outputCount:w});if(X.webgpu.validateInputContent&&h0(h,B),u.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");n.compute(Hi(h,B))}}),uc=Pe(()=>{St(),Rt(),Or=n=>{if(n[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(n[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(n[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(n[0].dims[2]!==n[1].dims[0])throw new Error("last dimension of input and bias are not the same")},b0=n=>{let u=n[0].dims,h=n[0].dims[2],w=He.size(u)/4,B=n[0].dataType,v=gA("input",B,u,4),y=gA("bias",B,[h],4),F=gA("residual",B,u,4),N=JA("output",B,u,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:u,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(w/64)}}),getShaderSource:Y=>`
  const channels = ${h}u / 4;
  ${Y.declareVariables(v,y,F,N)}

  ${Y.mainStart()}
    ${Y.guardAgainstOutOfBoundsWorkgroupSizes(w)}
    let value = ${v.getByOffset("global_idx")}
      + ${y.getByOffset("global_idx % channels")} + ${F.getByOffset("global_idx")};
    ${N.setByOffset("global_idx","value")}
  }`}},I0=n=>{Or(n.inputs),n.compute(b0(n.inputs))}}),o1=Pe(()=>{Dt(),St(),qr(),Rt(),It=(n,u,h,w,B,v,y)=>{let F=Math.ceil(u/4),N="";N=typeof B=="string"?`${B}(a)`:B("a");let Y=gA("inputData",h,[F],4),te=JA("outputData",w,[F],4),Q=[{name:"vec_size",type:"u32"}];return y&&Q.push(...y),`
      ${n.registerUniforms(Q).declareVariables(Y,te)}

  ${v??""}

  ${n.mainStart()}
    ${n.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}

    let a = ${Y.getByOffset("global_idx")};
    ${te.setByOffset("global_idx",N)}
  }`},Ht=(n,u,h,w,B,v=n.dataType,y,F)=>{let N=[{type:12,data:Math.ceil(He.size(n.dims)/4)}];return y&&N.push(...y),{name:u,shaderCache:{hint:B,inputDependencies:["type"]},getShaderSource:Y=>It(Y,He.size(n.dims),n.dataType,v,h,w,F),getRunData:Y=>({outputs:[{dims:n.dims,dataType:v}],dispatchGroup:{x:Math.ceil(He.size(Y[0].dims)/64/4)},programUniforms:N})}},vn=n=>{n.compute(Ht(n.inputs[0],"Abs","abs"))},ss=n=>{n.compute(Ht(n.inputs[0],"Acos","acos"))},w0=n=>{n.compute(Ht(n.inputs[0],"Acosh","acosh"))},Aa=n=>{n.compute(Ht(n.inputs[0],"Asin","asin"))},ps=n=>{n.compute(Ht(n.inputs[0],"Asinh","asinh"))},ta=n=>{n.compute(Ht(n.inputs[0],"Atan","atan"))},hs=n=>{n.compute(Ht(n.inputs[0],"Atanh","atanh"))},Ri=n=>$e(n),yn=(n,u)=>{let h;switch(u.to){case 10:h="vec4<f16>";break;case 1:h="vec4<f32>";break;case 12:h="vec4<u32>";break;case 6:h="vec4<i32>";break;case 9:h="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${u.to}`)}n.compute(Ht(n.inputs[0],"Cast",h,void 0,u.cacheKey,u.to))},k0=n=>{let u,h,w=n.length>=2&&n[1].data!==0,B=n.length>=3&&n[2].data!==0;switch(n[0].dataType){case 1:u=w?n[1].getFloat32Array()[0]:-34028234663852886e22,h=B?n[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:u=w?n[1].getUint16Array()[0]:64511,h=B?n[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return $e({min:u,max:h})},Wi=(n,u)=>{let h=u||k0(n.inputs),w=jA(n.inputs[0].dataType);n.compute(Ht(n.inputs[0],"Clip",B=>`clamp(${B}, vec4<${w}>(uniforms.min), vec4<${w}>(uniforms.max))`,void 0,h.cacheKey,void 0,[{type:n.inputs[0].dataType,data:h.min},{type:n.inputs[0].dataType,data:h.max}],[{name:"min",type:w},{name:"max",type:w}]),{inputs:[0]})},vr=n=>{n.compute(Ht(n.inputs[0],"Ceil","ceil"))},xi=n=>{n.compute(Ht(n.inputs[0],"Cos","cos"))},En=n=>{n.compute(Ht(n.inputs[0],"Cosh","cosh"))},ra=n=>$e(n),ni=(n,u)=>{let h=jA(n.inputs[0].dataType);n.compute(Ht(n.inputs[0],"Elu",w=>`elu_vf32(${w})`,`
  const elu_alpha_ = ${h}(${u.alpha});

  fn elu_f32(a: ${h}) -> ${h} {
  return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0);
  }

  fn elu_vf32(v: vec4<${h}>) -> vec4<${h}> {
  return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));
  }`,u.cacheKey))},ms=(n="f32")=>`
const r0: ${n} = 0.3275911;
const r1: ${n} = 0.254829592;
const r2: ${n} = -0.284496736;
const r3: ${n} = 1.421413741;
const r4: ${n} = -1.453152027;
const r5: ${n} = 1.061405429;

fn erf_vf32(v: vec4<${n}>) -> vec4<${n}> {
  let absv = abs(v);
  let x = 1.0 / (1.0 + r0 * absv);
  return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));
}`,Mn=n=>{let u=jA(n.inputs[0].dataType);n.compute(Ht(n.inputs[0],"Erf",h=>`erf_vf32(${h})`,ms(u)))},Gn=n=>{n.compute(Ht(n.inputs[0],"Exp","exp"))},Dn=n=>{n.compute(Ht(n.inputs[0],"Floor","floor"))},Vi=n=>{let u=jA(n.inputs[0].dataType);n.compute(Ht(n.inputs[0],"Gelu",h=>`0.5 * ${h} * (1.0 + erf_vf32(${h} * 0.7071067811865475))`,ms(u)))},is=(n,u)=>{let h=jA(n.inputs[0].dataType);n.compute(Ht(n.inputs[0],"LeakyRelu",w=>`select(leaky_relu_alpha_ * ${w}, ${w}, ${w} >= vec4<${h}>(0.0))`,`const leaky_relu_alpha_ = ${h}(${u.alpha});`,u.cacheKey))},ji=n=>{n.compute(Ht(n.inputs[0],"Not",u=>`!${u}`))},x0=n=>{n.compute(Ht(n.inputs[0],"Neg",u=>`-${u}`))},Qn=n=>{n.compute(Ht(n.inputs[0],"Reciprocal",u=>`1.0/${u}`))},Tn=n=>{let u=jA(n.inputs[0].dataType);n.compute(Ht(n.inputs[0],"Relu",h=>`select(vec4<${u}>(0.0), ${h}, ${h} > vec4<${u}>(0.0))`))},Cs=n=>{n.compute(Ht(n.inputs[0],"Sigmoid",u=>`(1.0 / (1.0 + exp(-${u})))`))},Ui=n=>$e(n),Lr=(n,u)=>{let h=jA(n.inputs[0].dataType);n.compute(Ht(n.inputs[0],"HardSigmoid",w=>`max(vec4<${h}>(0.0), min(vec4<${h}>(1.0), ${u.alpha} * ${w} + vec4<${h}>(${u.beta})))`,void 0,u.cacheKey))},Pn=n=>{n.compute(Ht(n.inputs[0],"Sin","sin"))},Ki=n=>{n.compute(Ht(n.inputs[0],"Sinh","sinh"))},Bi=n=>{n.compute(Ht(n.inputs[0],"Sqrt","sqrt"))},za=n=>{n.compute(Ht(n.inputs[0],"Tan","tan"))},vi=n=>`sign(${n}) * (1 - exp(-2 * abs(${n}))) / (1 + exp(-2 * abs(${n})))`,zn=n=>{n.compute(Ht(n.inputs[0],"Tanh",vi))},_a=(n="f32")=>`
const fast_gelu_a: ${n} = 0.5;
const fast_gelu_b: ${n} = 0.7978845608028654;
const fast_gelu_c: ${n} = 0.035677408136300125;

fn tanh_v(v: vec4<${n}>) -> vec4<${n}> {
  return ${vi("v")};
}
`,Fn=n=>`(fast_gelu_a + fast_gelu_a * tanh_v(${n} * (fast_gelu_c * ${n} * ${n} + fast_gelu_b))) * ${n}`,Zi=n=>{let u=jA(n.inputs[0].dataType);n.compute(Ht(n.inputs[0],"FastGelu",Fn,_a(u),void 0,n.inputs[0].dataType))},Sn=(n,u)=>{let h=jA(n.inputs[0].dataType);return n.compute(Ht(n.inputs[0],"ThresholdedRelu",w=>`select(vec4<${h}>(0.0), ${w}, ${w} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${h}>(${u.alpha});`,u.cacheKey)),0},oi=n=>{n.compute(Ht(n.inputs[0],"Log","log"))},Nn=(n,u)=>`
const alpha = vec4<${n}>(${u});
const one = ${n}(1.0);
const zero = ${n}(0.0);

fn quick_gelu_impl(x: vec4<${n}>) -> vec4<${n}> {
  let v = x *alpha;
  var x1 : vec4<${n}>;
  for (var i = 0; i < 4; i = i + 1) {
    if (v[i] >= zero) {
      x1[i] = one / (one + exp(-v[i]));
    } else {
      x1[i] = one - one / (one + exp(v[i]));
    }
  }
  return x * x1;
}
`,B0=n=>`quick_gelu_impl(${n})`,v0=(n,u)=>{let h=jA(n.inputs[0].dataType);n.compute(Ht(n.inputs[0],"QuickGelu",B0,Nn(h,u.alpha),u.cacheKey,n.inputs[0].dataType))}}),dc=Pe(()=>{St(),Rt(),o1(),aa=n=>{if(n[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(n[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(n[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(n[0].dims[2]!==n[1].dims[0])throw new Error("last dimension of input and bias are not the same")},yi=n=>{let u=n[0].dims.slice();u[2]=u[2]/2;let h=gA("input",n[0].dataType,n[0].dims,4),w=gA("bias",n[0].dataType,[n[0].dims[2]],4),B=JA("output",n[0].dataType,u,4),v=He.size(u)/4,y=jt(n[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:u,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(v/64)}}),getShaderSource:F=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${n[0].dims[2]/4/2}u;

  ${F.declareVariables(h,w,B)}

  ${ms(y)}

  ${F.mainStart()}
    ${F.guardAgainstOutOfBoundsWorkgroupSizes(v)}
    let biasIdx = global_idx % halfChannels;
    let batchIndex = global_idx / halfChannels;
    let inputOffset = biasIdx + batchIndex * halfChannels * 2;
    let valueLeft = input[inputOffset] + bias[biasIdx];
    let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels];
    let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1);

    ${B.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},ha=n=>{aa(n.inputs),n.compute(yi(n.inputs))}}),fc=Pe(()=>{Dt(),St(),Rt(),On=(n,u,h,w,B,v,y,F,N,Y,te,Q)=>{let ie,oe;typeof F=="string"?ie=oe=(De,Le)=>`${F}((${De}),(${Le}))`:typeof F=="function"?ie=oe=F:(ie=F.scalar,oe=F.vector);let de,fe=JA("outputData",te,w.length,4),ke=gA("aData",N,u.length,4),Me=gA("bData",Y,h.length,4);if(B)if(v){let De=He.size(u)===1,Le=He.size(h)===1,Xe=u.length>0&&u[u.length-1]%4==0,_e=h.length>0&&h[h.length-1]%4==0;de=De||Le?fe.setByOffset("global_idx",oe(De?`${ke.type.value}(${ke.getByOffset("0")}.x)`:ke.getByOffset("global_idx"),Le?`${Me.type.value}(${Me.getByOffset("0")}.x)`:Me.getByOffset("global_idx"))):`
            let outputIndices = ${fe.offsetToIndices("global_idx * 4u")};
            let offsetA = ${ke.broadcastedIndicesToOffset("outputIndices",fe)};
            let offsetB = ${Me.broadcastedIndicesToOffset("outputIndices",fe)};
            ${fe.setByOffset("global_idx",oe(y||Xe?ke.getByOffset("offsetA / 4u"):`${ke.type.value}(${ke.getByOffset("offsetA / 4u")}[offsetA % 4u])`,y||_e?Me.getByOffset("offsetB / 4u"):`${Me.type.value}(${Me.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else de=fe.setByOffset("global_idx",oe(ke.getByOffset("global_idx"),Me.getByOffset("global_idx")));else{if(!v)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let De=(Le,Xe,_e="")=>{let lA=`aData[indexA${Xe}][componentA${Xe}]`,LA=`bData[indexB${Xe}][componentB${Xe}]`;return`
            let outputIndices${Xe} = ${fe.offsetToIndices(`global_idx * 4u + ${Xe}u`)};
            let offsetA${Xe} = ${ke.broadcastedIndicesToOffset(`outputIndices${Xe}`,fe)};
            let offsetB${Xe} = ${Me.broadcastedIndicesToOffset(`outputIndices${Xe}`,fe)};
            let indexA${Xe} = offsetA${Xe} / 4u;
            let indexB${Xe} = offsetB${Xe} / 4u;
            let componentA${Xe} = offsetA${Xe} % 4u;
            let componentB${Xe} = offsetB${Xe} % 4u;
            ${Le}[${Xe}] = ${_e}(${ie(lA,LA)});
          `};de=te===9?`
            var data = vec4<u32>(0);
            ${De("data",0,"u32")}
            ${De("data",1,"u32")}
            ${De("data",2,"u32")}
            ${De("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:`
            ${De("outputData[global_idx]",0)}
            ${De("outputData[global_idx]",1)}
            ${De("outputData[global_idx]",2)}
            ${De("outputData[global_idx]",3)}
          `}return`
        ${n.registerUniform("vec_size","u32").declareVariables(ke,Me,fe)}

        ${Q??""}

        ${n.mainStart()}
        ${n.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${de}
      }`},Xi=(n,u,h,w,B,v,y=h.dataType)=>{let F=h.dims.map(fe=>Number(fe)??1),N=w.dims.map(fe=>Number(fe)??1),Y=!He.areEqual(F,N),te=F,Q=He.size(F),ie=!1,oe=!1,de=[Y];if(Y){let fe=NA.calcShape(F,N,!1);if(!fe)throw new Error("Can't perform binary op on the given tensors");te=fe.slice(),Q=He.size(te);let ke=He.size(F)===1,Me=He.size(N)===1,De=F.length>0&&F[F.length-1]%4==0,Le=N.length>0&&N[N.length-1]%4==0;de.push(ke),de.push(Me),de.push(De),de.push(Le);let Xe=1;for(let _e=1;_e<te.length;_e++){let lA=F[F.length-_e];if(lA!==N[N.length-_e])break;Xe*=lA}Xe%4==0?(oe=!0,ie=!0):(ke||Me||De||Le)&&(ie=!0)}else ie=!0;return de.push(ie),{name:n,shaderCache:{hint:u+de.map(fe=>fe.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:fe=>On(fe,F,N,te,ie,Y,oe,B,h.dataType,w.dataType,y,v),getRunData:()=>({outputs:[{dims:te,dataType:y}],dispatchGroup:{x:Math.ceil(Q/64/4)},programUniforms:[{type:12,data:Math.ceil(He.size(te)/4)},...KA(F,N,te)]})}},Va=(n,u,h,w,B,v)=>{n.compute(Xi(u,B??"",n.inputs[0],n.inputs[1],h,w,v))},fr=n=>{Va(n,"Add",(u,h)=>`${u}+${h}`)},Ln=n=>{Va(n,"Div",(u,h)=>`${u}/${h}`)},Ms=n=>{Va(n,"Equal",{scalar:(u,h)=>`u32(${u}==${h})`,vector:(u,h)=>`vec4<u32>(${u}==${h})`},void 0,void 0,9)},li=n=>{Va(n,"Mul",(u,h)=>`${u}*${h}`)},y0=n=>{let u=gA("input",n.inputs[0].dataType,n.inputs[0].dims).type.value;Va(n,"Pow",{scalar:(h,w)=>`pow_custom(${h},${w})`,vector:(h,w)=>`pow_vector_custom(${h},${w})`},`
    fn pow_custom(a : ${u}, b : ${u}) -> ${u} {
      if (b == ${u}(0.0)) {
        return ${u}(1.0);
      } else if (a < ${u}(0.0) && f32(b) != floor(f32(b))) {
        return ${u}(pow(f32(a), f32(b))); // NaN
      }
      return select(sign(a), ${u}(1.0), round(f32(abs(b) % ${u}(2.0))) != 1.0) * ${u}(${u==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));
    }
    fn pow_vector_custom(a : vec4<${u}>, b : vec4<${u}>) -> vec4<${u}> {
      // TODO: implement vectorized pow
      return vec4<${u}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
    }
      `)},Yn=n=>{Va(n,"Sub",(u,h)=>`${u}-${h}`)},Hn=n=>{Va(n,"Greater",{scalar:(u,h)=>`u32(${u}>${h})`,vector:(u,h)=>`vec4<u32>(${u}>${h})`},void 0,void 0,9)},E0=n=>{Va(n,"Less",{scalar:(u,h)=>`u32(${u}<${h})`,vector:(u,h)=>`vec4<u32>(${u}<${h})`},void 0,void 0,9)},Gs=n=>{Va(n,"GreaterOrEqual",{scalar:(u,h)=>`u32(${u}>=${h})`,vector:(u,h)=>`vec4<u32>(${u}>=${h})`},void 0,void 0,9)},$a=n=>{Va(n,"LessOrEqual",{scalar:(u,h)=>`u32(${u}<=${h})`,vector:(u,h)=>`vec4<u32>(${u}<=${h})`},void 0,void 0,9)}}),pc=Pe(()=>{Dt(),St(),qr(),Rt(),Rn=(n,u)=>{if(!n||n.length<1)throw new Error("too few inputs");let h=n[0],w=h.dataType,B=h.dims.length;n.forEach((v,y)=>{if(y!==0){if(v.dataType!==w)throw new Error("input tensors should be one type");if(v.dims.length!==B)throw new Error("input tensors should have the same shape");v.dims.forEach((F,N)=>{if(N!==u&&F!==h.dims[N])throw new Error("non concat dimensions must match")})}})},Ei=(n,u)=>`
  fn calculateInputIndex(index: u32) -> u32 {
    let sizeInConcatAxis = array<u32, ${n}u>(${u});
    for (var i: u32 = 0u; i < ${n}; i += 1u ) {
      if (index < sizeInConcatAxis[i]) {
        return i;
      }
    }
    return ${n}u;
  }`,Zr=(n,u)=>{let h=n.length,w=[];for(let B=0;B<h;++B){let v=u.setByOffset("global_idx",n[B].getByIndices("indices"));h===1?w.push(v):B===0?w.push(`if (inputIndex == ${B}u) { ${v} }`):B===h-1?w.push(`else { ${v} }`):w.push(`else if (inputIndex == ${B}) { ${v} }`)}return w.join(`
`)},ar=(n,u,h,w)=>{let B=He.size(h),v=new Array(n.length),y=new Array(n.length),F=0,N=[],Y=[],te=[{type:12,data:B}];for(let de=0;de<n.length;++de)F+=n[de].dims[u],v[de]=F,Y.push(n[de].dims.length),y[de]=gA(`input${de}`,w,Y[de]),N.push("rank"),te.push({type:12,data:v[de]});for(let de=0;de<n.length;++de)te.push(...KA(n[de].dims));te.push(...KA(h));let Q=JA("output",w,h.length),ie=Q.indicesGet("indices",u),oe=Array.from(Array(v.length).keys()).map(de=>`uniforms.sizeInConcatAxis${de}`).join(",");return{name:"Concat",shaderCache:{hint:`${u}`,inputDependencies:N},getRunData:()=>({outputs:[{dims:h,dataType:w}],dispatchGroup:{x:Math.ceil(B/64)},programUniforms:te}),getShaderSource:de=>`

  ${(()=>{de.registerUniform("outputSize","u32");for(let fe=0;fe<n.length;fe++)de.registerUniform(`sizeInConcatAxis${fe}`,"u32");return de.declareVariables(...y,Q)})()}

  ${Ei(v.length,oe)}

  ${de.mainStart()}
    ${de.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

    var indices = ${Q.offsetToIndices("global_idx")};

    let inputIndex = calculateInputIndex(${ie});
    if (inputIndex != 0u) {
      let sizeInConcatAxis = array<u32, ${v.length}u>(${oe});
      ${ie} -= sizeInConcatAxis[inputIndex - 1u];
    }

    ${Zr(y,Q)}
  }`}},ci=(n,u)=>{let h=n.inputs,w=h[0].dims,B=He.normalizeAxis(u.axis,w.length);Rn(h,B);let v=w.slice();v[B]=h.reduce((F,N)=>F+(N.dims.length>B?N.dims[B]:0),0);let y=h.filter(F=>He.size(F.dims)>0);n.compute(ar(y,B,v,h[0].dataType),{inputs:y})},M0=n=>$e({axis:n.axis})}),Zn=Pe(()=>{Dt(),St(),Ds=(n,u,h="f32")=>{switch(n.activation){case"Relu":return`value = max(value, ${u}(0.0));`;case"Sigmoid":return`value = (${u}(1.0) / (${u}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${u}(${h}(uniforms.clip_min)), ${u}(${h}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${u}(0.0), min(${u}(1.0), ${h}(uniforms.alpha) * value + ${h}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${h}(uniforms.alpha) * value, value, value >= ${u}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value));
              value = sign(value) * (1.0 - e2x) / (1.0 + e2x);
        `;case"":return"";default:throw new Error(`Unsupported activation ${n.activation}`)}},Qs=(n,u)=>{n.activation==="Clip"?u.push({type:1,data:n.clipMax},{type:1,data:n.clipMin}):n.activation==="HardSigmoid"?u.push({type:1,data:n.alpha},{type:1,data:n.beta}):n.activation==="LeakyRelu"&&u.push({type:1,data:n.alpha})},Us=(n,u)=>{n.activation==="Clip"?u.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):n.activation==="HardSigmoid"?u.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):n.activation==="LeakyRelu"&&u.push({name:"alpha",type:"f32"})},Wn=n=>{let u=n?.activation||"";if(u==="HardSigmoid"){let[h,w]=n?.activation_params||[.2,.5];return{activation:u,alpha:h,beta:w}}if(u==="Clip"){let[h,w]=n?.activation_params||[Yt,Vt];return{activation:u,clipMax:w,clipMin:h}}if(u==="LeakyRelu"){let[h]=n?.activation_params||[.01];return{activation:u,alpha:h}}return{activation:u}}}),l1=Pe(()=>{ma=(n,u)=>{switch(n){case 1:return u;case 2:return`vec2<${u}>`;case 3:return`vec3<${u}>`;case 4:return`vec4<${u}>`;default:throw new Error(`${n}-component is not supported.`)}},G0=n=>`
      ${n?"value = value + getBiasByOutputCoords(coords);":""}
      `}),hc=Pe(()=>{Vn=n=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${n}.x), i32(${n}.y), i32(${n}.z), 1));
}
`}),c1=Pe(()=>{Dt(),St(),Rt(),Zn(),gi=(n,u,h,w,B)=>{let v=w-h;return`
      ${Array.from({length:h}).map((y,F)=>`
      if (${rt(u.shape,F,u.rank)} != 1) {
        ${u.indicesSet(n,F,rt(B,F+v,w))}
      } else {
        ${u.indicesSet(n,F,0)}
      }`).join("")}
`},Mi=(n,u,h,w,B=!1,v)=>{let y=n[0].dims,F=n[1].dims,N=y[y.length-2],Y=F[F.length-1],te=y[y.length-1],Q=Jt(Y),ie=Jt(te),oe=Jt(N),de=He.size(h)/Q/oe,fe=n.length>2,ke=w?w.slice(0,-2):h.slice(0,-2),Me=[He.size(ke),N,Y],De=[{type:12,data:de},{type:12,data:N},{type:12,data:Y},{type:12,data:te}];return Qs(u,De),De.push(...KA(ke,y,F)),fe&&De.push(...KA(n[2].dims)),De.push(...KA(Me)),{name:"MatMulNaive",shaderCache:{hint:`${u.activation};${Q};${ie};${oe};${B}`,inputDependencies:fe?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:v?v(h):h,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(de/64)},programUniforms:De}),getShaderSource:Le=>{let Xe=Nr("batch_dims",n[0].dataType,ke.length),_e=gA("a",n[0].dataType,y.length,ie),lA=gA("b",n[1].dataType,F.length,Q),LA=JA("output",n[0].dataType,Me.length,Q),st=jt(LA.type.tensor),ht=Ds(u,LA.type.value,st),pt=[_e,lA],mt="";if(fe){let tt=B?Q:1;pt.push(gA("bias",n[2].dataType,n[2].dims.length,tt)),mt=B?`value += bias[col / ${tt}];`:`value += ${LA.type.value}(bias[row + i]);`}let vt=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];return Us(u,vt),`
  ${Le.registerUniforms(vt).registerInternalVariables(Xe).declareVariables(...pt,LA)}
  ${Le.mainStart()}
    ${Le.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${Q})) * ${Q};
    var index1 = global_idx / (uniforms.N / ${Q});
    let stride1 = uniforms.M / ${oe};
    let row = (index1 % stride1) * ${oe};
    let batch = index1 / stride1;

    ${h.length===2?"":`let batch_indices = ${Xe.offsetToIndices("batch")};`}

    var a_indices: ${_e.type.indices};
    ${gi("a_indices",_e,_e.rank-2,Xe.rank,"batch_indices")}
    ${_e.indicesSet("a_indices",_e.rank-2,0)}
    ${_e.indicesSet("a_indices",_e.rank-1,0)}
    let a_offset = ${_e.indicesToOffset("a_indices")};

    var b_indices: ${lA.type.indices};
    ${gi("b_indices",lA,lA.rank-2,Xe.rank,"batch_indices")}
    ${lA.indicesSet("b_indices",lA.rank-2,0)}
    ${lA.indicesSet("b_indices",lA.rank-1,0)}
    let b_offset = ${lA.indicesToOffset("b_indices")};
    var values: array<${LA.type.value}, ${oe}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${ie}) {
      ${(()=>{let tt=`var a_data: ${_e.type.value};`;for(let lt=0;lt<ie;lt++)tt+=`
              let b_data${lt} = b[(b_offset + (k + ${lt}) * uniforms.N + col) / ${Q}];`;for(let lt=0;lt<oe;lt++){tt+=`a_data = a[(a_offset + (row + ${lt}) * uniforms.K + k) / ${ie}];`;for(let wA=0;wA<ie;wA++)tt+=`
            values[${lt}] = fma(${lA.type.value}(a_data${ie===1?"":`[${wA}]`}), b_data${wA}, values[${lt}]);
`}return tt})()}
    }
    for (var i = 0u; i < ${oe}u; i++) {
      var value = values[i];
      ${mt}
      ${ht}
      let cur_indices = ${LA.type.indices}(batch, row + i, col);
      let offset = ${LA.indicesToOffset("cur_indices")};
      ${LA.setByOffset(`offset / ${Q}`,"value")};
    }
  }
  `}}}}),g1=Pe(()=>{Dt(),St(),Rt(),Zn(),c1(),l1(),sa=(n,u)=>n?`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          kStart + inputRow,
          globalRowStart / innerElementSize + inputCol${u?", batchIndices":""});
        `:`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          globalRow + innerRow,
          kStart / innerElementSize + inputCol${u?", batchIndices":""});
        `,jn=(n,u)=>n?`
        let ACached0 = mm_Asub[k * innerElementSize][localRow];
        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];
        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];
        ${u===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}
        for (var i = 0; i < rowPerThread; i = i + 1) {
          acc[i] = BCached0 * ACached0[i] + acc[i];
          acc[i] = BCached1 * ACached1[i] + acc[i];
          acc[i] = BCached2 * ACached2[i] + acc[i];
          ${u===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}
        }`:`
        for (var i = 0; i < rowPerThread; i = i + 1) {
          let ACached = mm_Asub[tileRow + i][k];
          acc[i] = BCached0 * ACached.x + acc[i];
          acc[i] = BCached1 * ACached.y + acc[i];
          acc[i] = BCached2 * ACached.z + acc[i];
          ${u===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}
        }`,qi=(n,u,h="f32",w,B=!1,v=32,y=!1,F=32)=>{let N=u[1]*n[1],Y=u[0]*n[0],te=B?N:v,Q=B?v:N,ie=te/u[0],oe=v/u[1];if((!B||ie!==4||n[1]!==4)&&(B||ie!==3&&ie!==4)||te%u[0]!=0||v%u[1]!=0||n[0]!==4)throw new Error(`If transposeA ${B} is true, innerElementSize ${ie} and workPerThread[1] ${n[1]} must be 4.
      Otherwise, innerElementSize ${ie} must be 3 or 4.
  tileAWidth ${te} must be divisible by workgroupSize[0]${u[0]}. tileInner ${v} must be divisible by workgroupSize[1] ${u[1]}. colPerThread ${n[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${ie}<${h}>, ${te/ie}>, ${Q}>;
var<workgroup> mm_Bsub: array<array<vec4<${h}>, ${Y/n[0]}>, ${v}>;

const rowPerThread = ${n[1]};
const colPerThread = ${n[0]};
const innerElementSize = ${ie};
const tileInner = ${v};

@compute @workgroup_size(${u[0]}, ${u[1]}, ${u[2]})
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
        @builtin(global_invocation_id) globalId : vec3<u32>,
        @builtin(workgroup_id) workgroupId : vec3<u32>) {
  let localRow = i32(localId.y);
  let tileRow = localRow * rowPerThread;
  let tileCol = i32(localId.x);

  let globalRow =i32(globalId.y) * rowPerThread;
  let globalCol = i32(globalId.x);
  let batch = ${y?"0":"i32(globalId.z)"};
  ${w?`let batchIndices = ${w.offsetToIndices("u32(batch)")};`:""}
  let globalRowStart = i32(workgroupId.y) * ${N};

  let num_tiles = ${y?`${Math.ceil(F/v)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
  var kStart = ${y?`i32(globalId.z) * ${F}`:"0"};

  var acc: array<vec4<${h}>, rowPerThread>;

  // Loop over shared dimension.
  let tileRowB = localRow * ${oe};
  for (var t = 0; t < num_tiles; t = t + 1) {
      // Load one tile of A into local memory.
      for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
          let inputRow = tileRow + innerRow;
          let inputCol = tileCol;
          ${sa(B,w)}
      }

      // Load one tile of B into local memory.
      for (var innerRow = 0; innerRow < ${oe}; innerRow = innerRow + 1) {
          let inputRow = tileRowB + innerRow;
          let inputCol = tileCol;
          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${w?", batchIndices":""});
      }
      kStart = kStart + tileInner;
      workgroupBarrier();

      // Compute acc values for a single thread.
      for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {
          let BCached0 = mm_Bsub[k * innerElementSize][tileCol];
          let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];
          let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];
          ${ie===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}

          ${jn(B,ie)}
      }

      workgroupBarrier();
  }

  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
  }
}`},Ks=(n,u)=>n?`
            mm_Asub[inputRow][inputCol] = mm_readA(batch,
              kStart + inputRow,
              globalRowStart + inputCol${u?", batchIndices":""});
            `:`
            mm_Asub[inputRow][inputCol] = mm_readA(batch,
              globalRowStart + inputRow,
              kStart + inputCol${u?", batchIndices":""});
            `,D0=n=>n?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",ui=(n,u,h="f32",w,B=!1,v=32,y=!1,F=32,N=!1)=>{let Y=n[1]*u[1],te=n[0]*u[0],Q=B?Y:v,ie=B?v:Y;if(ie%u[1]!=0||Q%u[0]!=0||v%u[1]!=0)throw new Error(`tileAHight ${ie} must be divisible by workgroupSize[1]${u[1]}, tileAWidth ${Q} must be divisible by workgroupSize[0]${u[0]}, tileInner ${v} must be divisible by workgroupSize[1]${u[1]}`);let oe=ie/u[1],de=Q/u[0],fe=v/u[1],ke=N?`
    let localRow = i32(localId.y);
    let localCol = i32(localId.x);
    let globalRowStart = i32(workgroupId.y) * ${Y};
    let globalColStart = i32(workgroupId.x) * ${te};

    // Loop over shared dimension.
    for (var t = 0; t < num_tiles; t = t + 1) {
      // Load one tile of A into local memory.
      for (var inputRow = localRow; inputRow < ${ie}; inputRow = inputRow + ${u[1]}) {
        for (var inputCol = localCol; inputCol < ${Q}; inputCol = inputCol + ${u[0]}) {
          ${Ks(B,w)}
        }
      }
      // Load one tile of B into local memory.
      for (var inputRow = localRow; inputRow < ${v}; inputRow = inputRow + ${u[1]}) {
            for (var inputCol = localCol; inputCol < ${te}; inputCol = inputCol + ${u[0]}) {
          mm_Bsub[inputRow][inputCol] = mm_readB(batch,
            kStart + inputRow,
            globalColStart + inputCol${w?", batchIndices":""});
        }
      }
      kStart = kStart + tileInner;
      workgroupBarrier();

      // Compute acc values for a single thread.
      var BCached : array<${h}, colPerThread>;
      for (var k = 0; k < tileInner; k = k + 1) {
        for (var inner = 0; inner < colPerThread; inner = inner + 1) {
          BCached[inner] = mm_Bsub[k][localCol + inner * ${u[0]}];
        }
        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
          let ACached = ${B?`mm_Asub[k][localRow + innerRow * ${u[1]}];`:`mm_Asub[localRow + innerRow * ${u[1]}][k];`}
          for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
            acc[innerRow][innerCol] = acc[innerRow][innerCol] +
                ACached * BCached[innerCol];
          }
        }
      }
      workgroupBarrier();
    }
    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      let gRow = globalRowStart + localRow + innerRow * ${u[1]};
      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
        let gCol = globalColStart + localCol + innerCol * ${u[0]};
        mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);
      }
    }
    `:`
let tileRow = i32(localId.y) * rowPerThread;
let tileCol = i32(localId.x) * colPerThread;

let globalRow = i32(globalId.y) * rowPerThread;
let globalCol = i32(globalId.x) * colPerThread;
let globalRowStart = i32(workgroupId.y) * ${Y};

let tileRowA = i32(localId.y) * ${oe};
let tileColA = i32(localId.x) * ${de};
let tileRowB = i32(localId.y) * ${fe};
// Loop over shared dimension.
for (var t = 0; t < num_tiles; t = t + 1) {
  // Load one tile of A into local memory.
  for (var innerRow = 0; innerRow < ${oe}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < ${de}; innerCol = innerCol + 1) {
      let inputRow = tileRowA + innerRow;
      let inputCol = tileColA + innerCol;
      ${Ks(B,w)}
    }
  }

  // Load one tile of B into local memory.
  for (var innerRow = 0; innerRow < ${fe}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
      let inputRow = tileRowB + innerRow;
      let inputCol = tileCol + innerCol;
      mm_Bsub[inputRow][inputCol] = mm_readB(batch,
        kStart + inputRow,
        globalCol + innerCol${w?", batchIndices":""});
    }
  }
  kStart = kStart + tileInner;
  workgroupBarrier();

  // Compute acc values for a single thread.
  var BCached : array<${h}, colPerThread>;
  for (var k = 0; k < tileInner; k = k + 1) {
    for (var inner = 0; inner < colPerThread; inner = inner + 1) {
      BCached[inner] = mm_Bsub[k][tileCol + inner];
    }

    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      ${D0(B)}
      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
      }
    }
  }

  workgroupBarrier();
}

for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
    mm_write(batch, globalRow + innerRow, globalCol + innerCol,
        acc[innerRow][innerCol]);
  }
}
`;return`
  var<workgroup> mm_Asub : array<array<${h}, ${Q}>, ${ie}>;
  var<workgroup> mm_Bsub : array<array<${h}, ${te}>, ${v}>;
  const rowPerThread = ${n[1]};
  const colPerThread = ${n[0]};
  const tileInner = ${v};

@compute @workgroup_size(${u[0]}, ${u[1]}, ${u[2]})
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
        @builtin(global_invocation_id) globalId : vec3<u32>,
        @builtin(workgroup_id) workgroupId : vec3<u32>) {
    let batch = ${y?"0":"i32(globalId.z)"};
    ${w?`let batchIndices = ${w.offsetToIndices("u32(batch)")};`:""}
    let num_tiles = ${y?`${Math.ceil(F/v)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
    var kStart = ${y?`i32(globalId.z) * ${F}`:"0"};

    var acc : array<array<${h}, colPerThread>, rowPerThread>;
    ${ke}
  }
`},ct=(n,u,h,w,B=!1)=>{let[v,y,F,N]=w,Y=jt(w[0].type.tensor);return`
    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${v.type.indices}) -> ${ma(n,Y)} {
      var value = ${ma(n,Y)}(0.0);
      let col = colIn * ${n};
      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
      {
        var aIndices: ${y.type.indices};
        ${gi("aIndices",y,y.rank-2,v.rank,"batchIndices")}
        ${y.indicesSet("aIndices",y.rank-2,"u32(row)")}
        ${y.indicesSet("aIndices",y.rank-1,"u32(colIn)")}
        value = ${y.getByIndices("aIndices")};
      }
      return value;
    }

    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${v.type.indices}) -> ${ma(n,Y)} {
      var value = ${ma(n,Y)}(0.0);
      let col = colIn * ${n};
      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)
      {
        var bIndices: ${F.type.indices};
        ${gi("bIndices",F,F.rank-2,v.rank,"batchIndices")}
        ${F.indicesSet("bIndices",F.rank-2,"u32(row)")}
        ${F.indicesSet("bIndices",F.rank-1,"u32(colIn)")}
        value = ${F.getByIndices("bIndices")};
      }
      return value;
    }

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${ma(n,Y)}) {
      let col = colIn * ${n};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
        var value = valueIn;
        let coords = vec3<i32>(batch, row, colIn);
        ${u?`value = value + ${B?"bias[colIn]":`${ma(n,Y)}(bias[row])`};`:""}
        ${h}
        ${N.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},A=(n,u,h,w,B=!1,v)=>{let y=n[0].dims,F=n[1].dims,N=y.slice(0,-2),Y=F.slice(0,-2),te=w?w.slice(0,-2):h.slice(0,-2),Q=He.size(te),ie=y[y.length-2],oe=y[y.length-1],de=F[F.length-1],fe=oe%4==0&&de%4==0,ke=ie<=8?[4,1,1]:[4,4,1],Me=[8,8,1],De=[Math.ceil(de/Me[0]/ke[0]),Math.ceil(ie/Me[1]/ke[1]),Math.ceil(Q/Me[2]/ke[2])],Le=fe?4:1,Xe=[...N,ie,oe/Le],_e=Xe.length,lA=[...Y,oe,de/Le],LA=lA.length,st=[Q,ie,de/Le],ht=[{type:6,data:ie},{type:6,data:de},{type:6,data:oe}];Qs(u,ht),ht.push(...KA(te,Xe,lA));let pt=["rank","rank"],mt=n.length>2;return mt&&(ht.push(...KA(n[2].dims)),pt.push("rank")),ht.push(...KA(st)),{name:"MatMul",shaderCache:{hint:`${ke};${u.activation};${fe};${B}`,inputDependencies:pt},getRunData:()=>({outputs:[{dims:v?v(h):h,dataType:n[0].dataType}],dispatchGroup:{x:De[0],y:De[1],z:De[2]},programUniforms:ht}),getShaderSource:vt=>{let tt=te.length,lt=Nr("batchDims",n[0].dataType,tt,1),wA=jt(n[0].dataType),UA=gA("a",n[0].dataType,_e,Le),At=gA("b",n[1].dataType,LA,Le),yt=JA("result",n[0].dataType,st.length,Le),gr=[UA,At];if(mt){let Na=B?Le:1;gr.push(gA("bias",n[2].dataType,n[2].dims.length,Na))}let la=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Us(u,la);let zi=jt(yt.type.tensor),$n=Ds(u,yt.type.value,zi),Jo=ct(Le,mt,$n,[lt,UA,At,yt],B);return`
  ${vt.registerUniforms(la).registerInternalVariables(lt).declareVariables(...gr,yt)}
  ${Jo}
  ${fe?qi(ke,Me,wA,lt):ui(ke,Me,wA,lt)}
                   `}}}}),mc=Pe(()=>{Dt(),Ti(),Rt(),Zn(),l1(),hc(),g1(),t=(n,u,h,w,B=!1,v,y=4,F=4,N=4,Y="f32")=>{let te=st=>{switch(st){case 1:return"return w[row * i32(uniforms.w_shape[3]) + colIn];";case 4:return"return w[row * i32(uniforms.w_shape[3]) / 4 + colIn];";default:throw new Error(`innerElementSize ${st} is not supported.`)}},Q=n?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,ie=n?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,oe=n?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",de=n?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",fe=n?"row":"col",ke=n?"col":"row",Me=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${n?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${fe} / outWidth;
    let outCol = ${fe} % outWidth;

    let WRow = ${ke} / (i32(uniforms.w_shape[1]) * inChannels);
    let WCol = ${ke} / inChannels % i32(uniforms.w_shape[1]);
    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
    let xCh = ${ke} % inChannels;
    var resData = ${ma(y,Y)}(0.0);
    // The bounds checking is always needed since we use it to pad zero for
    // the 'same' padding type.
    if (xRow >= 0 && xRow < ${oe} && xCol >= 0 && xCol < ${de}) {
      ${Q}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${(st=>{switch(st){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${Y}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${st} is not supported.`)}})(y)}
    }
    return resData;`,De=n?u&&w?`
    let col = colIn * ${y};
    ${Me}`:`
    let col = colIn * ${y};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${Me}
    }
    return ${ma(y,Y)}(0.0);`:w&&h?`
    let col = colIn * ${y};
    ${Me}`:`
    let col = colIn * ${y};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${Me}
    }
    return ${ma(y,Y)}(0.0);`,Le=n?w&&h?te(F):`
    let col = colIn * ${F};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${te(F)}
    }
    return ${ma(F,Y)}(0.0);`:`
    let col = colIn * ${F};
    if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) {
      ${te(F)}
    }
    return ${ma(F,Y)}(0.0);`,Xe=ma(N,Y),_e=ma(n?y:F,Y),lA=ma(n?F:y,Y),LA=Ds(v,Xe,Y);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${_e} {
      ${n?De:Le}
    }

    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${lA} {
      ${n?Le:De}
    }

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${Xe}) {
      let col = colIn * ${N};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
      {
      var value = valueIn;
      let outWidth = ${n?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${ie}
      ${G0(B)}
      ${LA}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},r=(n,u,h,w,B,v,y,F,N)=>{let Y=u.format==="NHWC",te=Y?n[0].dims[3]:n[0].dims[1],Q=h[0],ie=Y?h[2]:h[3],oe=Y?h[1]:h[2],de=Y?h[3]:h[1],fe=Y&&(te%4==0||te%3==0)&&de%4==0,ke=Y?de:ie*oe,Me=Y?ie*oe:de,De=[8,8,1],Le=w<=8?[4,1,1]:[4,4,1],Xe=[Math.ceil(ke/De[0]/Le[0]),Math.ceil(Me/De[1]/Le[1]),Math.ceil(Q/De[2]/Le[2])];Pt("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${Xe}`);let _e=fe?Y&&te%4!=0?3:4:1,lA=De[1]*Le[1],LA=De[0]*Le[0],st=Math.max(De[0]*_e,De[1]),ht=w%lA==0,pt=B%LA==0,mt=v%st==0,vt=fe?[_e,4,4]:[1,1,1],tt=[{type:6,data:w},{type:6,data:B},{type:6,data:v},{type:6,data:[u.pads[0],u.pads[1]]},{type:6,data:u.strides},{type:6,data:u.dilations}];Qs(u,tt),tt.push(...KA(n[0].dims,n[1].dims));let lt=["rank","rank"];return y&&(tt.push(...KA(n[2].dims)),lt.push("rank")),tt.push(...KA(h)),{name:"Conv2DMatMul",shaderCache:{hint:`${u.cacheKey};${_e};${fe};${ht};${pt};${mt};${lA};${LA};${st}`,inputDependencies:lt},getRunData:()=>({outputs:[{dims:N?N(h):h,dataType:n[0].dataType}],dispatchGroup:{x:Xe[0],y:Xe[1],z:Xe[2]},programUniforms:tt}),getShaderSource:wA=>{let UA=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];Us(u,UA);let At=fe?4:1,yt=jt(n[0].dataType),gr=`
      fn setOutputAtIndex(flatIndex : i32, value : ${fe?`vec4<${yt}>`:yt}) {
        result[flatIndex] = ${fe?`vec4<${yt}>`:yt}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${fe?`vec4<${yt}>`:yt}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${fe?"/ 4":""}, value);
      }`,la=[gA("x",n[0].dataType,n[0].dims.length,_e===3?1:_e),gA("w",n[1].dataType,n[1].dims.length,At)],zi=JA("result",n[0].dataType,h.length,At);if(y){let $n=gA("bias",n[2].dataType,n[2].dims.length,At);la.push($n),gr+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${fe?`vec4<${yt}>`:yt} {
          return bias[coords.${Y?"w":"y"}${fe?"/ 4":""}];
        }`}return`
        ${Vn("uniforms.result_strides")}
        //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,
        //  outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,
        //  dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };
        ${wA.registerUniforms(UA).declareVariables(...la,zi)}
        ${gr}
        ${t(Y,ht,pt,mt,y,u,vt[0],vt[1],vt[2],yt)}
        ${fe?qi(Le,De,yt,void 0,!Y,st):ui(Le,De,yt,void 0,!Y,st,!1,void 0,F)}`}}}}),Cc=Pe(()=>{Dt(),Ti(),St(),Rt(),Zn(),l1(),a=n=>{let u=1;for(let h=0;h<n.length;h++)u*=n[h];return u},s=n=>typeof n=="number"?[n,n,n]:n,i=(n,u)=>u<=1?n:n+(n-1)*(u-1),o=(n,u,h,w=1)=>{let B=i(u,w);return Math.floor((n[0]*(h-1)-h+B)/2)},c=(n,u,h,w,B)=>{B==null&&(B=o(n,u[0],w[0]));let v=[0,0,0,h];for(let y=0;y<3;y++)n[y]+2*B>=u[y]&&(v[y]=Math.trunc((n[y]-u[y]+2*B)/w[y]+1));return v},f=(n,u,h,w,B,v,y,F,N,Y)=>{let te,Q,ie,oe;if(n==="VALID"&&(n=0),typeof n=="number"){te={top:n,bottom:n,left:n,right:n,front:n,back:n};let de=c([u,h,w,1],[F,N,Y],1,[B,v,y],n);Q=de[0],ie=de[1],oe=de[2]}else if(Array.isArray(n)){if(!n.every((fe,ke,Me)=>fe===Me[0]))throw Error(`Unsupported padding parameter: ${n}`);te={top:n[0],bottom:n[1],left:n[2],right:n[3],front:n[4],back:n[5]};let de=c([u,h,w,1],[F,N,Y],1,[B,v,y],n[0]);Q=de[0],ie=de[1],oe=de[2]}else{if(n!=="SAME_UPPER")throw Error(`Unknown padding parameter: ${n}`);{Q=Math.ceil(u/B),ie=Math.ceil(h/v),oe=Math.ceil(w/y);let de=(Q-1)*B+F-u,fe=(ie-1)*v+N-h,ke=(oe-1)*y+Y-w,Me=Math.floor(de/2),De=de-Me,Le=Math.floor(fe/2),Xe=fe-Le,_e=Math.floor(ke/2);te={top:Le,bottom:Xe,left:_e,right:ke-_e,front:Me,back:De}}}return{padInfo:te,outDepth:Q,outHeight:ie,outWidth:oe}},m=(n,u,h,w,B,v=!1,y="channelsLast")=>{let F,N,Y,te,Q;if(y==="channelsLast")[F,N,Y,te,Q]=n;else{if(y!=="channelsFirst")throw new Error(`Unknown dataFormat ${y}`);[F,Q,N,Y,te]=n}let[ie,,oe,de,fe]=u,[ke,Me,De]=s(h),[Le,Xe,_e]=s(w),lA=i(oe,Le),LA=i(de,Xe),st=i(fe,_e),{padInfo:ht,outDepth:pt,outHeight:mt,outWidth:vt}=f(B,N,Y,te,ke,Me,De,lA,LA,st),tt=v?ie*Q:ie,lt=[0,0,0,0,0];return y==="channelsFirst"?lt=[F,tt,pt,mt,vt]:y==="channelsLast"&&(lt=[F,pt,mt,vt,tt]),{batchSize:F,dataFormat:y,inDepth:N,inHeight:Y,inWidth:te,inChannels:Q,outDepth:pt,outHeight:mt,outWidth:vt,outChannels:tt,padInfo:ht,strideDepth:ke,strideHeight:Me,strideWidth:De,filterDepth:oe,filterHeight:de,filterWidth:fe,effectiveFilterDepth:lA,effectiveFilterHeight:LA,effectiveFilterWidth:st,dilationDepth:Le,dilationHeight:Xe,dilationWidth:_e,inShape:n,outShape:lt,filterShape:u}},p=(n,u,h,w,B,v)=>{let y=v==="channelsLast";y?n[0].dims[3]:n[0].dims[1];let F={x:h.map((ie,oe)=>oe)},N=[Math.ceil(a(F.x.map(ie=>h[ie]))/64),1,1];Pt("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${N}`);let Y=[{type:12,data:He.size(h)},{type:12,data:w},{type:12,data:B},{type:12,data:u.strides},{type:12,data:u.dilations}];Qs(u,Y),Y.push(...KA(n[0].dims,n[1].dims));let te=["rank","rank"],Q=n.length===3;return Q&&(Y.push(...KA(n[2].dims)),te.push("rank")),Y.push(...KA(h)),{name:"Conv3DNaive",shaderCache:{hint:`${u.cacheKey};${y};1;${Q}`,inputDependencies:te},getRunData:()=>({outputs:[{dims:h,dataType:n[0].dataType}],dispatchGroup:{x:N[0],y:N[1],z:N[2]},programUniforms:Y}),getShaderSource:ie=>{let oe=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:w.length},{name:"pads",type:"u32",length:B.length},{name:"strides",type:"u32",length:u.strides.length},{name:"dilations",type:"u32",length:u.dilations.length}];Us(u,oe);let de=jt(n[0].dataType),fe=gA("x",n[0].dataType,n[0].dims.length,1),ke=gA("W",n[1].dataType,n[1].dims.length,1),Me=[fe,ke],De=JA("result",n[0].dataType,h.length,1),Le="";if(Q){let lA=gA("bias",n[2].dataType,n[2].dims.length,1);Me.push(lA),Le+=`
        fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${de} {
          return bias[${rt("coords",y?4:1,5)}];
        }`}let Xe=ma(1,de),_e=Ds(u,Xe,de);return`
            ${Le}
            fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${fe.getByIndices("aIndices")};
            }
            fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${ke.getByIndices("aIndices")};
            }
          ${ie.registerUniforms(oe).declareVariables(...Me,De)}
          ${ie.mainStart()}
          ${ie.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
              let coords = ${De.offsetToIndices("global_idx")};
              let batch = ${rt("coords",0,fe.rank)};
              let d2 = ${rt("coords",y?fe.rank-1:1,fe.rank)};
              let xFRCCorner = vec3<u32>(${rt("coords",y?1:2,fe.rank)},
              ${rt("coords",y?2:3,fe.rank)},
              ${rt("coords",y?3:4,fe.rank)}) * uniforms.strides - uniforms.pads;
              let xFCorner = xFRCCorner.x;
              let xRCorner = xFRCCorner.y;
              let xCCorner = xFRCCorner.z;
              let xShapeY = ${rt("uniforms.x_shape",y?1:2,fe.rank)};
              let xShapeZ = ${rt("uniforms.x_shape",y?2:3,fe.rank)};
              let xShapeW = ${rt("uniforms.x_shape",y?3:4,fe.rank)};
              let xShapeU = ${rt("uniforms.x_shape",y?4:1,fe.rank)};
              let inputDepthNearestVec4 = (xShapeU / 4) * 4;
              let inputDepthVec4Remainder = xShapeU % 4;

              var value = 0.0;
              for (var wF = 0u; wF < uniforms.filter_dims[0]; wF++) {
                let xF = xFCorner + wF * uniforms.dilations[0];
                if (xF < 0 || xF >= xShapeY) {
                  continue;
                }

                for (var wR = 0u; wR < uniforms.filter_dims[1]; wR++) {
                  let xR = xRCorner + wR * uniforms.dilations[1];
                  if (xR < 0 || xR >= xShapeZ) {
                    continue;
                  }

                  for (var wC = 0u; wC < uniforms.filter_dims[2]; wC++) {
                    let xC = xCCorner + wC * uniforms.dilations[2];
                    if (xC < 0 || xC >= xShapeW) {
                      continue;
                    }

                    for (var d1 = 0u; d1 < inputDepthNearestVec4; d1 += 4) {
                      ${y?`let xValues = vec4<f32>(
                               getX(batch, xF, xR, xC, d1),
                               getX(batch, xF, xR, xC, d1 + 1),
                               getX(batch, xF, xR, xC, d1 + 2),
                               getX(batch, xF, xR, xC, d1 + 3));
                            `:`let xValues = vec4<f32>(
                               getX(batch, d1, xF, xR, xC),
                               getX(batch, d1 + 1, xF, xR, xC),
                               getX(batch, d1 + 2, xF, xR, xC),
                               getX(batch, d1 + 3, xF, xR, xC));
                            `}
                            let wValues = vec4<f32>(
                              getW(d2, d1, wF, wR, wC),
                              getW(d2, d1 + 1, wF, wR, wC),
                              getW(d2, d1 + 2, wF, wR, wC),
                              getW(d2, d1 + 3, wF, wR, wC));
                      value += dot(xValues, wValues);
                    }
                    if (inputDepthVec4Remainder == 1) {
                        ${y?`value += getX(batch, xF, xR, xC, inputDepthNearestVec4)
                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`:`value += getX(batch, inputDepthNearestVec4, xF, xR, xC)
                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`}
                    } else if (inputDepthVec4Remainder == 2) {
                      ${y?`let xValues = vec2<f32>(
                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1));
                      `:`let xValues = vec2<f32>(
                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC));
                    `}
                    let wValues = vec2<f32>(
                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC));
                      value += dot(xValues, wValues);
                    } else if (inputDepthVec4Remainder == 3) {
                      ${y?`let xValues = vec3<f32>(
                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2));
                      `:`let xValues = vec3<f32>(
                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 2, xF, xR, xC));
                    `}
                    let wValues = vec3<f32>(
                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 2, wF, wR, wC));
                      value += dot(xValues, wValues);
                    }
                  }
                }
              }
              ${Q?"value = value + getBiasByOutputCoords(coords)":""};
              ${_e}
              result[global_idx] = f32(value);
          }`}}}}),bc=Pe(()=>{Dt(),St(),Rt(),Zn(),I=(n,u,h,w)=>{let B=n.length>2,v=B?"value += b[output_channel];":"",y=n[0].dims,F=n[1].dims,N=u.format==="NHWC",Y=N?h[3]:h[1],te=Y/u.group,Q=N&&te>=4?Jt(Y):1,ie=He.size(h)/Q,oe=[{type:12,data:ie},{type:12,data:u.dilations},{type:12,data:[u.strides[0],u.strides[1]]},{type:12,data:[u.pads[0],u.pads[1]]},{type:12,data:te}];Qs(u,oe),oe.push(...KA(y,[F[0],F[1],F[2],F[3]/Q]));let de=B?["rank","rank","rank"]:["rank","rank"];return oe.push(...KA([h[0],h[1],h[2],h[3]/Q])),{name:"GroupedConv",shaderCache:{hint:`${u.cacheKey}_${Q}`,inputDependencies:de},getRunData:()=>({outputs:[{dims:w?w(h):h,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(ie/64)},programUniforms:oe}),getShaderSource:fe=>{let ke=JA("output",n[0].dataType,h.length,Q),Me=jt(ke.type.tensor),De=Ds(u,ke.type.value,Me),Le=gA("x",n[0].dataType,y.length),Xe=gA("w",n[1].dataType,F.length,Q),_e=[Le,Xe];B&&_e.push(gA("b",n[2].dataType,n[2].dims,Q));let lA=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:u.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];Us(u,lA);let LA=N?`
      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) {
        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];

        if (xHeight < 0u || xHeight >= uniforms.x_shape[1]) {
          continue;
        }

        for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[1]; wWidth++) {
          let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];
          if (xWidth < 0u || xWidth >= uniforms.x_shape[2]) {
            continue;
          }

          for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) {
            let input_channel = in_channel_offset + wInChannel;
            let xVal = ${Le.get("batch","xHeight","xWidth","input_channel")};
            let wVal = ${Xe.get("wHeight","wWidth","wInChannel","output_channel")};
            value += xVal * wVal;
          }
        }
      }
      `:`
      for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {
        let input_channel = in_channel_offset + wInChannel;
        for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {
          let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];

          if (xHeight < 0u || xHeight >= uniforms.x_shape[2]) {
            continue;
          }

          for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) {
            let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];
            if (xWidth < 0u || xWidth >= uniforms.x_shape[3]) {
              continue;
            }

            let xVal = ${Le.get("batch","input_channel","xHeight","xWidth")};
            let wVal = ${Xe.get("output_channel","wInChannel","wHeight","wWidth")};
            value += xVal * wVal;
          }
        }
      }
      `;return`
  ${fe.registerUniforms(lA).declareVariables(..._e,ke)}

  ${fe.mainStart()}
    ${fe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

    let outputIndices = ${ke.offsetToIndices("global_idx")};
    let batch: u32 = outputIndices[0];
    let output_channel: u32 = outputIndices[${N?3:1}];
    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${N?1:2}], outputIndices[${N?2:3}]) * uniforms.strides - uniforms.pads;
    let group_id: u32 = output_channel * ${Q} / uniforms.output_channels_per_group;
    var in_channel_offset = group_id * uniforms.w_shape[${N?2:1}];

    var value: ${ke.type.value} = ${ke.type.value}(0);
    ${LA}
    ${v}
    ${De}
    ${ke.setByOffset("global_idx","value")}
  }`}}},E=(n,u,h,w)=>{let B=n.length>2,v=Jt(h[3]),y=Jt(h[2]),F=He.size(h)/v/y,N=[n[0].dims[0],n[0].dims[1],n[0].dims[2],n[0].dims[3]/v],Y=[n[1].dims[0],n[1].dims[1],n[1].dims[2],n[1].dims[3]/v],te=[h[0],h[1],h[2],h[3]/v],Q=[{type:12,data:F},{type:6,data:[u.strides[0],u.strides[1]]},{type:6,data:[u.pads[0],u.pads[1]]}];Qs(u,Q),Q.push(...KA(N,Y,te));let ie=(y-1)*u.strides[1]+Y[1];return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${u.cacheKey};${v};${y};${ie};${Y[0]};${Y[1]}`,inputDependencies:B?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:w?w(h):h,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(F/64)},programUniforms:Q}),getShaderSource:oe=>{let de=JA("output",n[0].dataType,te.length,v),fe=jt(de.type.tensor),ke=Ds(u,de.type.value,fe),Me=gA("x",n[0].dataType,N.length,v),De=gA("w",n[1].dataType,Y.length,v),Le=[Me,De];B&&Le.push(gA("b",n[2].dataType,n[2].dims,v));let Xe=B?"value += b[output_channel];":"",_e=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Us(u,_e),`
  ${oe.registerUniforms(_e).declareVariables(...Le,de)}
  ${oe.mainStart()}
    ${oe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let width0 = uniforms.output_shape[3];
    let output_channel = global_idx % width0;
    var index1 = global_idx / width0;
    let width1 = uniforms.output_shape[2] / ${y}u;
    let col = (index1 % width1) * ${y}u;
    index1 = index1 / width1;
    let row = index1 % uniforms.output_shape[1];
    let batch = index1 / uniforms.output_shape[1];

    let x_corner = vec2<i32>(i32(row), i32(col)) * uniforms.strides - uniforms.pads;

    var x_vals: array<${Me.type.value}, ${ie}>;
    var values: array<${de.type.value}, ${y}>;
    let input_channel = output_channel;
    // Use constant instead of uniform can give better performance for w's height/width.
    for (var w_height: u32 = 0u; w_height < ${Y[0]}; w_height++) {
      let x_height = x_corner.x + i32(w_height);
      if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {
        for (var i = 0; i < ${ie}; i++) {
          let x_width = x_corner.y + i;
          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
            x_vals[i] = ${Me.get("batch","u32(x_height)","u32(x_width)","input_channel")};
          } else {
            x_vals[i] = ${Me.type.value}(0);
          }
        }
        for (var w_width: u32 = 0u; w_width < ${Y[1]}; w_width++) {
          let w_val = ${De.get("w_height","w_width","0","output_channel")};
          for (var i = 0u; i < ${y}u; i++) {
            values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);
          }
        }
      }
    }

    for (var i = 0u; i < ${y}u; i++) {
      var value = values[i];
      ${Xe}
      ${ke}
      ${de.set("batch","row","col + i","output_channel","value")};
    }
  }`}}}}),Nt=Pe(()=>{St(),mc(),Cc(),g1(),bc(),Zn(),c1(),tn(),T=(n,u,h,w,B,v)=>{let y=n[0],F=n.slice(v?1:2,v?3:4),N=F.length,Y=u[0],te=u.slice(2).map((ie,oe)=>ie+(ie-1)*(h[oe]-1)),Q=F.map((ie,oe)=>ie+w[oe]+w[oe+N]).map((ie,oe)=>Math.floor((ie-te[oe]+B[oe])/B[oe]));return Q.splice(0,0,y),Q.splice(v?3:1,0,Y),Q},L=[2,3,1,0],Z=(n,u)=>{if(!n||n.length!==2&&n.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(n[0].dims.length>5)throw new Error("greater than 5D is not supported");if(n[0].dims.length!==n[1].dims.length)throw new Error("filter does not have same dimension as input");if(n[0].dims[u.format==="NHWC"?n[0].dims.length-1:1]!==n[1].dims[1]*u.group)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(n.length===3&&(n[2].dims.length!==1||n[1].dims[0]!==n[2].dims[0]))throw new Error("invalid bias");let h=n[0].dims.length-2;if(u.dilations.length!==h)throw new Error(`dilations should be ${h}D`);if(u.strides.length!==h)throw new Error(`strides should be ${h}D`);if(u.pads.length!==2*h)throw new Error(`pads should be ${2*h}D`);if(u.kernelShape.length!==0&&u.kernelShape.length!==n[1].dims.length-2)throw new Error("invalid kernel shape")},ee=(n,u)=>{let h=n.kernelShape.slice();h.length<u[1].dims.length-2&&h.push(...Array(u[1].dims.length-2-h.length).fill(0));for(let v=2;v<u[1].dims.length;++v)h[v-2]===0&&(h[v-2]=u[1].dims[v]);let w=n.pads.slice();VA.adjustPadsBasedOnAutoPad(u[0].dims,n.strides,n.dilations,h,w,n.format==="NHWC",n.autoPad);let B=Object.assign({},n);return Object.assign(B,{kernelShape:h,pads:w}),B},ce=n=>{let u=Wn(n),h=n.format;return{autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][n.auto_pad],format:h,dilations:n.dilations,group:n.group,kernelShape:n.kernel_shape,pads:n.pads,strides:n.strides,wIsConst:n.w_is_const(),...u,cacheKey:`${n.format};${u.activation};`}},he=(n,u,h,w)=>{let B=h.format==="NHWC",v=T(u[0].dims,u[1].dims,h.dilations,h.pads,h.strides,B);if(h.group!==1){let _e=[u[0]];if(B){let lA=n.kernelCustomData.wT??n.compute(Ma(u[1],L),{inputs:[1],outputs:[h.wIsConst?-2:-1]})[0];h.wIsConst&&!n.kernelCustomData.wT&&(n.kernelCustomData.wT=lA),_e.push(lA)}else _e.push(u[1]);return u.length===3&&_e.push(u[2]),void(!n.adapterInfo.isArchitecture("ampere")&&B&&u[1].dims[0]===h.group&&u[1].dims[1]===1&&h.dilations[0]===1&&h.dilations[1]===1?n.compute(E(_e,h,v,w),{inputs:_e}):n.compute(I(_e,h,v,w),{inputs:_e}))}let y=u.length===3,F=u[0].dims[B?1:2],N=u[0].dims[B?2:3],Y=u[0].dims[B?3:1],te=u[1].dims[2],Q=u[1].dims[3],ie=v[B?1:2],oe=v[B?2:3],de=v[B?3:1],fe=B&&te===F&&Q===N&&h.pads[0]===0&&h.pads[1]===0;if(fe||te===1&&Q===1&&h.dilations[0]===1&&h.dilations[1]===1&&h.strides[0]===1&&h.strides[1]===1&&h.pads[0]===0&&h.pads[1]===0){let _e,lA,LA,st=v[0],ht=[];if(B){let vt=n.kernelCustomData.wT??n.compute(Ma(u[1],L),{inputs:[1],outputs:[h.wIsConst?-2:-1]})[0];if(h.wIsConst&&!n.kernelCustomData.wT&&(n.kernelCustomData.wT=vt),fe){let tt=F*N*Y;_e=u[0].reshape([1,st,tt]),lA=vt.reshape([1,tt,de]),LA=[1,st,de]}else _e=u[0].reshape([st,F*N,Y]),lA=vt.reshape([1,Y,de]),LA=[st,ie*oe,de];ht.push(_e),ht.push(lA)}else _e=u[0].reshape([st,Y,F*N]),lA=u[1].reshape([1,de,Y]),LA=[st,de,ie*oe],ht.push(lA),ht.push(_e);y&&ht.push(u[2]);let pt=LA[2],mt=ht[0].dims[ht[0].dims.length-1];return void(pt<8&&mt<8?n.compute(Mi(ht,h,v,LA,B,w),{inputs:ht}):n.compute(A(ht,h,v,LA,B,w),{inputs:ht}))}let ke=n.kernelCustomData.wT??n.compute(Ma(u[1],L),{inputs:[1],outputs:[h.wIsConst?-2:-1]})[0];h.wIsConst&&!n.kernelCustomData.wT&&(n.kernelCustomData.wT=ke);let Me=[u[0],ke];y&&Me.push(u[2]);let De=B?ie*oe:de,Le=B?de:ie*oe,Xe=te*Q*Y;n.compute(r(Me,h,v,De,Le,Xe,y,!0,w),{inputs:Me})},ve=(n,u)=>{let h=u.format==="NHWC",w=[n.inputs[0].reshape(h?[n.inputs[0].dims[0],1,n.inputs[0].dims[1],n.inputs[0].dims[2]]:[n.inputs[0].dims[0],n.inputs[0].dims[1],1,n.inputs[0].dims[2]]),n.inputs[1].reshape([n.inputs[1].dims[0],n.inputs[1].dims[1],1,n.inputs[1].dims[2]])];n.inputs.length===3&&w.push(n.inputs[2]);let B=[0,u.pads[0],0,u.pads[1]],v=[1].concat(u.strides),y=[1].concat(u.dilations),F=[1].concat(u.kernelShape),N=ee({...u,pads:B,strides:v,dilations:y,kernelShape:F},w);he(n,w,N,Y=>h?[Y[0],Y[2],Y[3]]:[Y[0],Y[1],Y[3]])},Qe=(n,u,h)=>{let w=h.format==="NHWC"?"channelsLast":"channelsFirst",B=ee(h,u),v=h.autoPad==="NOTSET"?h.pads:h.autoPad,y=m(u[0].dims,u[1].dims,h.strides,h.dilations,v,!1,w);n.compute(p(u,B,y.outShape,[y.filterDepth,y.filterHeight,y.filterWidth],[y.padInfo.front,y.padInfo.top,y.padInfo.left],w))},We=(n,u)=>{if(Z(n.inputs,u),n.inputs[0].dims.length===3)ve(n,u);else if(n.inputs[0].dims.length===5)Qe(n,n.inputs,u);else{let h=ee(u,n.inputs);he(n,n.inputs,h)}}}),N1=Pe(()=>{Dt(),Ti(),St(),Rt(),Ke=(n,u,h)=>{let w=n.length>2,B=u.outputShape,v=u.format==="NHWC",y=u.group,F=n[1].dims,N=F[2]/y,Y=F[3],te=v?Jt(N):1,Q=v?Jt(Y):1,ie=v?Y===1?te:Q:1,oe=He.size(B)/Q,de=[Math.ceil(oe/64),1,1];Pt("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${de}`);let fe=["rank","rank"],ke=[u.strides[0],u.strides[1]],Me=[u.kernelShape[v?1:2],u.kernelShape[v?2:3]],De=[u.dilations[0],u.dilations[1]],Le=[Me[0]+(u.dilations[0]<=1?0:(u.kernelShape[v?1:2]-1)*(u.dilations[0]-1)),Me[1]+(u.dilations[1]<=1?0:(u.kernelShape[v?2:3]-1)*(u.dilations[1]-1))],Xe=[Le[0]-1-Math.floor((u.pads[0]+u.pads[2])/2),Le[1]-1-Math.floor((u.pads[1]+u.pads[3])/2)],_e=[{type:12,data:oe},{type:12,data:ke},{type:12,data:Me},{type:12,data:De},{type:12,data:Le},{type:6,data:Xe},{type:12,data:N},{type:12,data:Y},...KA(n[0].dims,n[1].dims)];return w&&(_e.push(...KA(n[2].dims)),fe.push("rank")),_e.push(...KA(B)),{name:"ConvTranspose2D",shaderCache:{hint:`${u.cacheKey};${te}${ie}${Q}${Y===1}`,inputDependencies:fe},getRunData:()=>({dispatchGroup:{x:de[0],y:de[1],z:de[2]},outputs:[{dims:h?h(B):B,dataType:n[0].dataType}],programUniforms:_e}),getShaderSource:lA=>{let LA=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:ke.length},{name:"filter_dims",type:"u32",length:Me.length},{name:"dilations",type:"u32",length:Me.length},{name:"effective_filter_dims",type:"u32",length:Le.length},{name:"pads",type:"i32",length:Xe.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],st=jt(n[0].dataType),ht=v?1:2,pt=v?2:3,mt=v?3:1,vt=gA("W",n[1].dataType,n[1].dims.length,ie),tt=gA("Dy",n[0].dataType,n[0].dims.length,te),lt=[tt,vt];w&&lt.push(gA("bias",n[2].dataType,[B[mt]].length,Q));let wA=JA("result",n[0].dataType,B.length,Q),UA=`
            let outputIndices = ${wA.offsetToIndices(`global_idx * ${Q}`)};
            let batch = ${wA.indicesGet("outputIndices",0)};
            let d1 = ${wA.indicesGet("outputIndices",mt)};
            let r = ${wA.indicesGet("outputIndices",ht)};
            let c = ${wA.indicesGet("outputIndices",pt)};
            let dyCorner = vec2<i32>(i32(r), i32(c)) - uniforms.pads;
            let dyRCorner = dyCorner.x;
            let dyCCorner = dyCorner.y;
            let groupId = d1 / uniforms.output_channels_per_group;
            let wOutChannel = d1 - groupId * uniforms.output_channels_per_group;
            // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
            // ? = to be determined. : = across all values in that axis.
            var dotProd = ${wA.type.value}(0.0);
            var wR: u32 = 0;
            if (uniforms.dilations.x == 1) {
              // Minimum wR >= 0 that satisfies (dyRCorner + wR) % (uniforms.strides.x) == 0
              wR = u32(((dyRCorner + i32(uniforms.strides.x) - 1) / i32(uniforms.strides.x)) * i32(uniforms.strides.x) - dyRCorner);
            }
            for (; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {
              if (wR % uniforms.dilations.x != 0) {
                continue;
              }
              let dyR = (${st}(dyRCorner) + ${st}(wR)) / ${st}(uniforms.strides[0]);
              let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;
              if (dyR < 0.0 || dyR >= ${st}(uniforms.Dy_shape[${ht}]) || fract(dyR) > 0.0 ||
                  wRPerm < 0) {
                continue;
              }
              let idyR: u32 = u32(dyR);
              var wC: u32 = 0;
              if (uniforms.dilations.y == 1) {
                // Minimum wC >= 0 that satisfies (dyCCorner + wC) % (uniforms.strides.y) == 0
                wC = u32(((dyCCorner + i32(uniforms.strides.y) - 1) / i32(uniforms.strides.y)) * i32(uniforms.strides.y) - dyCCorner);
              }

              for (; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
                if (wC % uniforms.dilations.y != 0) {
                  continue;
                }
                let dyC = (${st}(dyCCorner) + ${st}(wC)) / ${st}(uniforms.strides.y);
                let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
                if (dyC < 0.0 || dyC >= ${st}(uniforms.Dy_shape[${pt}]) ||
                    fract(dyC) > 0.0 || wCPerm < 0) {
                  continue;
                }
                let idyC: u32 = u32(dyC);
                var inputChannel = groupId * uniforms.input_channels_per_group;
                for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + ${te}) {
                  let xValue = ${v?tt.getByOffset(`${tt.indicesToOffset(`${tt.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${te}`):tt.get("batch","inputChannel","idyR","idyC")};
                  ${(()=>{let At="";if(te===1)At+=`
        let w_offset = ${vt.indicesToOffset(`${vt.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)};
        let wValue = ${vt.getByOffset(`w_offset / ${ie}`)};
        dotProd = dotProd + xValue * wValue;`;else if(Y===1)At+=`
          let wValue = ${vt.getByOffset(`${vt.indicesToOffset(`${vt.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)} / ${ie}`)};
          dotProd = dotProd + dot(xValue, wValue);`;else for(let yt=0;yt<te;yt++)At+=`
            let wValue${yt} = ${vt.getByOffset(`${vt.indicesToOffset(`${vt.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel + ${yt}, wOutChannel)`)} / ${ie}`)};
            dotProd = dotProd + xValue[${yt}] * wValue${yt};`;return At})()}
                  inputChannel = inputChannel + ${te};
                }
                wC = wC + uniforms.strides.y - 1;
              }
              wR = wR + uniforms.strides[0] - 1;
            }
            let value = dotProd${w?` + bias[d1 / ${Q}]`:""};
            ${wA.setByOffset("global_idx","value")};
          `;return`
    ${lA.registerUniforms(LA).declareVariables(...lt,wA)}
      ${lA.mainStart()}
      ${lA.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
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        ${Q.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${ie}
      }`}}},d2=n=>$e({axis:n.axis}),go=(n,u)=>{let h=n.inputs;g2(h),n.compute(u2(n.inputs,u))}}),K=Pe(()=>{Dt(),St(),Rt(),f2=(n,u,h,w,B,v,y,F,N)=>{let Y=[{type:12,data:v},{type:12,data:w},{type:12,data:B},{type:12,data:h},{type:12,data:y},{type:12,data:F},{type:12,data:N}],te=[v];return Y.push(...KA(u.dims,te)),n.compute({name:"computeSliceOffsets",shaderCache:{hint:`${B.length}_${h.length}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:te,dataType:n.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(v/64)},programUniforms:Y}),getShaderSource:Q=>{let ie=[gA("indices_data",u.dataType,u.dims.length),JA("input_slice_offsets_data",12,1,1)],oe=[{name:"output_size",type:"u32"},{name:"batch_dims",type:"u32"},{name:"input_dims",type:"u32",length:B.length},{name:"sizes_from_slice_dims_data",type:"u32",length:h.length},{name:"num_slices_per_batch",type:"u32"},{name:"input_batch_stride",type:"u32"},{name:"num_slice_dims",type:"u32"}];return`
  ${Q.registerUniforms(oe).declareVariables(...ie)}
  ${Q.mainStart()}
    ${Q.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let batch_idx = global_idx / uniforms.num_slices_per_batch;
    let base_offset = batch_idx * uniforms.input_batch_stride;

    let slice_indices_base_offset = global_idx * uniforms.num_slice_dims;
    var relative_slice_offset = 0;
    for (var dim_idx = 0u; dim_idx < uniforms.num_slice_dims; dim_idx ++) {
      var index = i32(indices_data[dim_idx + slice_indices_base_offset].x);
      let input_dim_idx = uniforms.batch_dims + dim_idx;
      if (index < 0) {
        ${B.length===1?"index += i32(uniforms.input_dims);":"index += i32(uniforms.input_dims[input_dim_idx]);"}
      }
      ${h.length===1?"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data);":"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data[dim_idx]);"}
    }

    input_slice_offsets_data[global_idx] =  base_offset + u32(relative_slice_offset);
  }`}},{inputs:[u],outputs:[-1]})[0]},p2=(n,u)=>{let h=n.inputs,w=h[0].dims,B=h[0].dataType,v=h[1].dims,y=v[v.length-1],F=He.sizeToDimension(v,v.length-1),N=He.sizeFromDimension(w,u.batchDims+y),Y=He.sizeToDimension(w,u.batchDims),te=He.sizeFromDimension(w,u.batchDims),Q=F/Y,ie=new Array(y),oe=N;for(let Le=0;Le<y;++Le)ie[y-1-Le]=oe,oe*=w[u.batchDims+y-1-Le];let de=f2(n,h[1],ie,u.batchDims,w,F,Q,te,y),fe=u.batchDims+y;if(fe>w.length)throw new Error("last dimension of indices must not be larger than rank of input tensor");let ke=v.slice(0,-1).concat(w.slice(fe)),Me=He.size(ke),De=[{type:12,data:Me},{type:12,data:N},...KA(h[0].dims,de.dims,ke)];n.compute({name:"GatherND",shaderCache:{hint:u.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:ke,dataType:B}],dispatchGroup:{x:Math.ceil(Me/64)},programUniforms:De}),getShaderSource:Le=>{let Xe=gA("data",h[0].dataType,h[0].dims.length),_e=gA("slice_offsets",12,de.dims.length),lA=JA("output",h[0].dataType,ke.length);return`
          ${Le.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(Xe,_e,lA)}
            ${Le.mainStart()}
            ${Le.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let slice_offset = slice_offsets[global_idx / uniforms.slice_size];
          output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size];
        }`}},{inputs:[h[0],de]})},uo=n=>({batchDims:n.batch_dims,cacheKey:""})}),U=Pe(()=>{Dt(),St(),qr(),Rt(),h2=(n,u)=>{if(n.length<3||n.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let h=He.normalizeAxis(u.quantizeAxis,n[0].dims.length),w=u.blockSize,B=n[0],v=n[2],y=n.length===4?n[3]:void 0;if(v.dims.length!==B.dims.length||!B.dims.map((F,N)=>N===h?Math.ceil(F/w)===v.dims[N]:F===v.dims[N]).reduce((F,N)=>F&&N,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(y){if(y.dataType!==B.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(y.dims.length!==v.dims.length||!y.dims.map((F,N)=>F===v.dims[N]).reduce((F,N)=>F&&N,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},m2=(n,u)=>{let h=n[0].dims,w=n[1].dims,B=h.length,v=He.normalizeAxis(u.gatherAxis,B),y=He.normalizeAxis(u.quantizeAxis,B),F=h.slice(0);F.splice(v,1,...w);let N=He.size(F),Y=n[2].dataType,te=n[0].dataType===22,Q=[{type:12,data:N},{type:12,data:y},{type:12,data:v},{type:12,data:u.blockSize},...KA(...n.map((ie,oe)=>ie.dims),F)];return{name:"GatherBlockQuantized",shaderCache:{hint:`${u.cacheKey};${n.filter((ie,oe)=>oe!==1).map(ie=>ie.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:n.length},(ie,oe)=>"rank")},getRunData:()=>({outputs:[{dims:F,dataType:Y}],dispatchGroup:{x:Math.ceil(N/64)},programUniforms:Q}),getShaderSource:ie=>{let oe=gA("data",n[0].dataType,n[0].dims.length),de=gA("inputIndices",n[1].dataType,n[1].dims.length),fe=gA("scales",n[2].dataType,n[2].dims.length),ke=n.length>3?gA("zeroPoint",n[3].dataType,n[3].dims.length):void 0,Me=JA("output",Y,F.length),De=[oe,de,fe];return ke&&De.push(ke),`
        ${ie.registerUniforms([{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}]).declareVariables(...De,Me)}
        ${ie.mainStart()}
        let output_indices = ${Me.offsetToIndices("global_idx")};
        var indices_indices = ${de.type.indices}(0);
        ${w.length>1?`
          for (var i: u32 = 0; i < ${w.length}; i++) {
            let index = ${Me.indicesGet("output_indices","uniforms.gather_axis + i")};
            ${de.indicesSet("indices_indices","i","index")};
          }`:`indices_indices = ${Me.indicesGet("output_indices","uniforms.gather_axis")};`};
        var data_indices = ${oe.type.indices}(0);
        for (var i: u32 = 0; i < uniforms.gather_axis; i++) {
          let index = ${Me.indicesGet("output_indices","i")};
          ${oe.indicesSet("data_indices","i","index")};
        }
        var index_from_indices = ${de.getByIndices("indices_indices")};
        if (index_from_indices < 0) {
          index_from_indices += ${h[v]};
        }
        ${oe.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")};
        for (var i = uniforms.gather_axis + 1; i < ${F.length}; i++) {
          let index = ${Me.indicesGet("output_indices",`i + ${w.length} - 1`)};
          ${oe.indicesSet("data_indices","i","index")};
        }
        let data_offset = ${oe.indicesToOffset("data_indices")};
        let data_index = data_offset % 8;
        // Convert 4-bit packed data to 8-bit packed data.
        let packed_4bit_quantized_data = ${oe.getByOffset("data_offset / 8")};
        let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f;
        let quantized_data_vec = ${te?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data));
        let quantized_data = quantized_data_vec[data_index / 2];
        var scale_indices = data_indices;
        let quantize_axis_index = ${fe.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size;
        ${fe.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")};
        var scale = ${fe.getByIndices("scale_indices")};
        ${ke?`
              let zero_point_indices = scale_indices;
              let zero_point_offset = ${ke.indicesToOffset("zero_point_indices")};
              let zero_point_index = zero_point_offset % 8;
              let packed_4bit_zero_points = ${ke.getByOffset("zero_point_offset / 8")};
              let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f;
              let zero_point_vec = ${te?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points));
              let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"};
        let dequantized_data = ${jA(Y)}(quantized_data - zero_point) * scale;
        ${Me.setByOffset("global_idx","dequantized_data")};
    }`}}},C2=(n,u)=>{let h=n.inputs;h2(h,u),n.compute(m2(n.inputs,u))},b2=n=>$e({blockSize:n.blockSize,gatherAxis:n.gatherAxis,quantizeAxis:n.quantizeAxis})}),Ee=Pe(()=>{Dt(),St(),qr(),Rt(),fo=n=>{if(!n||n.length!==2)throw new Error("GatherElements requires 2 inputs.");if(n[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(n[0].dims.length!==n[1].dims.length)throw new Error(`GatherElements requires that the data input and
                     indices input tensors be of same rank.`)},I2=(n,u)=>{let h=n[0].dims,w=n[0].dataType,B=h.length,v=n[1].dims,y=n[1].dataType,F=He.normalizeAxis(u.axis,B),N=h[F],Y=v.slice(0),te=He.size(Y),Q=gA("input",w,B),ie=gA("indicesInput",y,v.length),oe=JA("output",w,Y.length),de=[{type:12,data:te},{type:6,data:N},{type:12,data:F}];return de.push(...KA(h,v,Y)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:Y,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(te/64)},programUniforms:de}),getShaderSource:fe=>`
      ${fe.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(Q,ie,oe)}
      ${fe.mainStart()}
      ${fe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

      let outputIndices = ${oe.offsetToIndices("global_idx")};

      var idx = ${ie.getByOffset("global_idx")};
      if (idx < 0) {
        idx = idx + uniforms.axisDimLimit;
      }
      var inputIndices = ${Q.type.indices}(outputIndices);
      ${Q.indicesSet("inputIndices","uniforms.axis","u32(idx)")};
      let value = ${Q.getByIndices("inputIndices")};

      ${oe.setByOffset("global_idx","value")};
  }`}},w2=n=>$e({axis:n.axis}),po=(n,u)=>{let h=n.inputs;fo(h),n.compute(I2(n.inputs,u))}}),eA=Pe(()=>{Dt(),St(),Rt(),k2=n=>{if(!n)throw new Error("Input is missing");if(n.length<2||n.length>3)throw new Error("Invaid input number.");if(n.length===3&&n[2].dims.length>2)throw new Error("Invalid input shape of C");if(n[0].dataType!==n[1].dataType||n.length===3&&n[0].dataType!==n[2].dataType)throw new Error("Input types are mismatched")},x2=(n,u)=>{let h=n[0].dims.slice(),w=n[1].dims.slice(),[B,v,y]=bA.getShapeOfGemmResult(h,u.transA,w,u.transB,n.length===3?n[2].dims:void 0),F=[B,v];if(!F)throw new Error("Can't use gemm on the given tensors");let N=16,Y=Math.ceil(v/N),te=Math.ceil(B/N),Q=(He.size(F),[{type:12,data:Y},{type:12,data:B},{type:12,data:v},{type:12,data:y},{type:1,data:u.alpha},{type:1,data:u.beta}]),ie=["type","type"];return n.length===3&&(Q.push(...KA(n[2].dims)),ie.push("rank")),Q.push(...KA(F)),{name:"GemmShared",shaderCache:{hint:`${u.cacheKey}`,inputDependencies:ie},getRunData:()=>({outputs:[{dims:F,dataType:n[0].dataType}],dispatchGroup:{x:Y*te},programUniforms:Q}),getShaderSource:oe=>{let de=gA("a",n[0].dataType,n[0].dims),fe=gA("b",n[1].dataType,n[1].dims),ke=null,Me=[de,fe];n.length===3&&(ke=gA("c",n[2].dataType,n[2].dims.length),Me.push(ke));let De=JA("output",n[0].dataType,F.length);Me.push(De);let Le="",Xe="";u.transA&&u.transB?(Xe=`
      var col = tile_row_start + local_id.x;
      var row = k_start + local_id.y;
      if (col < uniforms.M && row < uniforms.K) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${de.type.value}(0);
      }

      col = k_start + local_id.x;
      row = tile_col_start + local_id.y;
      if (col < uniforms.K && row < uniforms.N) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${fe.type.value}(0);
      }
      `,Le="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):u.transA&&!u.transB?(Xe=`
      var col = tile_row_start + local_id.x;
      var row = k_start + local_id.y;
      if (col < uniforms.M && row < uniforms.K) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${de.type.value}(0);
      }

      col = tile_col_start + local_id.x;
      row = k_start + local_id.y;
      if (col < uniforms.N && row < uniforms.K) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${fe.type.value}(0);
      }
      `,Le="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!u.transA&&u.transB?(Xe=`
      var col = k_start + local_id.x;
      var row = tile_row_start + local_id.y;
      if (col < uniforms.K && row < uniforms.M) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${de.type.value}(0);
      }

      col = k_start + local_id.x;
      row = tile_col_start + local_id.y;
      if (col < uniforms.K && row < uniforms.N) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${fe.type.value}(0);
      }
      `,Le="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!u.transA&&!u.transB&&(Xe=`
      var col = k_start + local_id.x;
      var row = tile_row_start + local_id.y;
      if (col < uniforms.K && row < uniforms.M) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${de.type.value}(0);
      }

      col = tile_col_start + local_id.x;
      row = k_start + local_id.y;
      if (col < uniforms.N && row < uniforms.K) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${fe.type.value}(0);
      }
      `,Le="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let _e=u.alpha===1?"":"value *= uniforms.alpha;";return`
  ${oe.registerUniforms([{name:"num_tile_n",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}]).declareVariables(...Me)}
  var<workgroup> tile_a: array<array<${de.type.storage}, 16>, 16>;
  var<workgroup> tile_b: array<array<${fe.type.storage}, 16>, 16>;
  ${oe.mainStart([N,N,1])}
    let tile_col_start = (workgroup_index % uniforms.num_tile_n) * 16;
    let tile_row_start = (workgroup_index / uniforms.num_tile_n) * 16;
    let num_tiles = (uniforms.K - 1) / 16 + 1;
    var k_start = 0u;
    var value = ${De.type.value}(0);
    for (var t: u32 = 0u; t < num_tiles; t++) {
      ${Xe}
      k_start = k_start + 16;
      workgroupBarrier();

      for (var k: u32 = 0u; k < 16; k++) {
        ${Le}
      }
      workgroupBarrier();
    }

    ${_e}
    let m = tile_row_start + local_id.y;
    let n = tile_col_start + local_id.x;
    ${ke!=null?`let cOffset = ${ke.broadcastedIndicesToOffset("vec2(m, n)",De)}; value += ${De.type.value}(uniforms.beta) * ${ke.getByOffset("cOffset")};`:""}
    if (m < uniforms.M && n < uniforms.N) {
      output[m * uniforms.N + n] = value;
    }
  }`}}},ho=n=>({transA:n.transA,transB:n.transB,alpha:n.alpha,beta:n.beta,cacheKey:`${n.transA};${n.transB};${n.alpha===1}`}),B2=(n,u)=>{k2(n.inputs),n.compute(x2(n.inputs,u))}}),sA=Pe(()=>{Dt(),St(),qr(),Rt(),[Zs,Xs,_i,$i]=[0,1,2,3],v2=n=>{if(n[0].dims.length!==4)throw new Error("only 4-D tensor is supported.");if(n[0].dims.length!==n[1].dims.length)throw new Error("input dimensions must be equal to grid dimensions");if(n[0].dims.length-2!==n[1].dims[n[1].dims.length-1])throw new Error("last dimension of grid must be equal to "+(n[0].dims.length-2));if(n[0].dims[0]!==n[1].dims[0])throw new Error("grid batch size must match input batch size")},y2=n=>`
  fn gs_bicubic_interpolate(p: mat4x4<${n}>, x: f32, y: f32) -> ${n} {
    var v: vec4<f32>;
    var coeffs = gs_get_cubic_coeffs(x);
    for (var i = 0; i < 4; i++) {
      v[i] = coeffs[0] * p[i][0] + coeffs[1] * p[i][1] + coeffs[2] * p[i][2] + coeffs[3] * p[i][3];
    }
    coeffs = gs_get_cubic_coeffs(y);
    let pixel = ${n}(coeffs[0] * v[0] + coeffs[1] * v[1] + coeffs[2] * v[2] + coeffs[3] * v[3]);
    return pixel;
  }
`,mo=n=>`
  fn gs_denormalize(n: f32, length: i32) -> f32 {
    ${n.alignCorners===0?`
    // alignCorners: false => [-1, 1] to [-0.5, length - 0.5]
    return ((n + 1.0) * f32(length) - 1.0) / 2.0;
    `:`
    // alignCorners: true => [-1, 1] to [0, length - 1]
    return (n + 1.0) / 2.0 * (f32(length - 1));
    `}
  }
`,E2=n=>`
  ${n.paddingMode==="reflection"?`
      fn gs_reflect(x: i32, x_min: f32, x_max: f32) -> u32 {
        var dx = 0.0;
        var fx = f32(x);
        let range = x_max - x_min;
        if (fx < x_min) {
          dx = x_min - fx;
          let n = u32(dx / range);
          let r = dx - f32(n) * range;
          if (n % 2 == 0) {
            fx = x_min + r;
          } else {
            fx = x_max - r;
          }
        } else if (fx > x_max) {
          dx = fx - x_max;
          let n = u32(dx / range);
          let r = dx - f32(n) * range;
          if (n % 2 == 0) {
            fx = x_max - r;
          } else {
            fx = x_min + r;
          }
        }
        return u32(fx);
      }`:""}
`,M2=(n,u,h)=>`
  fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4<f32>) -> ${u} {
     var pixel = ${u}(0);
     var indices = vec4<u32>(0);
     indices[${Zs}] = batch;
     indices[${Xs}] = channel;`+(()=>{switch(h.paddingMode){case"zeros":return`
          if (r >= 0 && r < H && c >=0 && c < W) {
            indices[${_i}] = u32(r);
            indices[${$i}] = u32(c);
          }
        `;case"border":return`
          indices[${_i}] = u32(clamp(r, 0, H - 1));
          indices[${$i}] = u32(clamp(c, 0, W - 1));
        `;case"reflection":return`
          indices[${_i}] = gs_reflect(r, border[1], border[3]);
          indices[${$i}] = gs_reflect(c, border[0], border[2]);
        `;default:throw new Error(`padding mode ${h.paddingMode} is not supported`)}})()+`
    return ${n.getByIndices("indices")};
  }
`,G2=(n,u,h)=>(()=>{switch(h.mode){case"nearest":return`
          let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${Zs}], indices[${Xs}], border);
        `;case"bilinear":return`
          let x1 = i32(floor(x));
          let y1 = i32(floor(y));
          let x2 = x1 + 1;
          let y2 = y1 + 1;

          let p11 = pixel_at_grid(y1, x1, H_in, W_in, indices[${Zs}], indices[${Xs}], border);
          let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${Zs}], indices[${Xs}], border);
          let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${Zs}], indices[${Xs}], border);
          let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${Zs}], indices[${Xs}], border);

          let dx2 = ${u}(f32(x2) - x);
          let dx1 = ${u}(x - f32(x1));
          let dy2 = ${u}(f32(y2) - y);
          let dy1 = ${u}(y - f32(y1));
          let result = dy2 * (dx2 * p11 + dx1 * p12) + dy1 * (dx2 * p21 + dx1 * p22);
        `;case"bicubic":return`
          let x0 = i32(floor(x)) - 1;
          let y0 = i32(floor(y)) - 1;
          var p: mat4x4<${u}>;
          for (var h = 0; h < 4; h++) {
            for (var w = 0; w < 4; w++) {
              p[h][w] = pixel_at_grid(h + y0, w + x0, H_in, W_in, indices[${Zs}], indices[${Xs}], border);
            }
          }

          let dx = x - f32(x0 + 1);
          let dy = y - f32(y0 + 1);
          let result = gs_bicubic_interpolate(p, dx, dy);
        `;default:throw new Error(`mode ${h.mode} is not supported`)}})()+`${n.setByOffset("global_idx","result")}`,D2=(n,u)=>{let h=gA("x",n[0].dataType,n[0].dims.length),w=[n[1].dims[0],n[1].dims[1],n[1].dims[2]],B=gA("grid",n[1].dataType,w.length,2),v=[n[0].dims[0],n[0].dims[1],n[1].dims[1],n[1].dims[2]];u.format==="NHWC"&&(v=[n[0].dims[0],n[1].dims[1],n[1].dims[2],n[0].dims[3]],[Zs,Xs,_i,$i]=[0,3,1,2]);let y=JA("output",n[0].dataType,v.length),F=h.type.value,N=[{type:12,data:He.size(v)},...KA(n[0].dims,w,v)];return{name:"GridSample",shaderCache:{hint:`${u.cacheKey}`,inputDependencies:["type","type"]},getRunData:Y=>{let te=He.size(v);return{outputs:[{dims:v,dataType:Y[0].dataType}],dispatchGroup:{x:Math.ceil(te/64)},programUniforms:N}},getShaderSource:Y=>`
  ${Y.registerUniform("output_size","u32").declareVariables(h,B,y)}
  
  fn gs_get_cubic_coeffs(x: f32) -> vec4<f32> {
    let cubic_alpha = -0.75f;
    let x_abs = abs(x);
    var coeffs: vec4<f32>;
    coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha);
    coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1);
    coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1);
    coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha);
    return coeffs;
  }

  ${y2(F)}
  ${mo(u)}
  ${E2(u)}
  ${M2(h,F,u)}

  ${Y.mainStart()}
    ${Y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let H_in = i32(uniforms.x_shape[${_i}]);
      let W_in = i32(uniforms.x_shape[${$i}]);

      ${u.alignCorners===0?`
      let x_min = -0.5;
      let x_max = f32(W_in) - 0.5;
      let y_min = -0.5;
      let y_max = f32(H_in) - 0.5;
      `:`
      let x_min = 0.0;
      let x_max = f32(W_in) - 1.0;
      let y_min = 0.0;
      let y_max = f32(H_in) - 1.0;
      `};
      let border = vec4<f32>(x_min, y_min, x_max, y_max);

      let indices = ${y.offsetToIndices("global_idx")};
      var grid_indices = vec3<u32>(indices[${Zs}], indices[${_i}], indices[${$i}]);
      let nxy = ${B.getByIndices("grid_indices")};
      var x = gs_denormalize(f32(nxy[0]), W_in);
      var y = gs_denormalize(f32(nxy[1]), H_in);

      ${G2(y,F,u)}
  }`}},Q2=(n,u)=>{v2(n.inputs),n.compute(D2(n.inputs,u))},T2=n=>$e({alignCorners:n.align_corners,mode:n.mode,paddingMode:n.padding_mode,format:n.format})}),uA=Pe(()=>{Dt(),St(),qr(),s1(),n1(),Rt(),tn(),Ha=(n,u)=>n.length>u&&n[u].dims.length>0?n[u]:void 0,P2=(n,u)=>{let h=n[0],w=Ha(n,1),B=Ha(n,2),v=Ha(n,3),y=Ha(n,4),F=Ha(n,5),N=Ha(n,6),Y=Ha(n,7);if(h.dims.length!==3&&h.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let te,Q=h.dims[0],ie=h.dims[1],oe=h.dims.length===3?h.dims[2]:u.numHeads*h.dims[4],de=ie,fe=0,ke=0,Me=Math.floor(oe/u.numHeads);if(N&&Y&&He.size(N.dims)&&He.size(Y.dims)){if(N.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(N.dims[0]!==Q||N.dims[1]!==u.numHeads||N.dims[3]!==Me)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(Y.dims[0]!==Q||Y.dims[1]!==u.numHeads||Y.dims[3]!==Me)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(N.dims[2]!==Y.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(Y.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');fe=N.dims[2],ke=N.dims[2]}else if(N&&He.size(N.dims)||Y&&He.size(Y.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');if(w&&He.size(w.dims)>0){if(h.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(w.dims.length<3||w.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(h.dims[0]!==w.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(w.dims.length===3){if(w.dims[2]!==h.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');te=2,de=w.dims[1]}else if(w.dims.length===5){if(w.dims[2]!==u.numHeads||w.dims[3]!==2||w.dims[4]!==Me)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(B)throw new Error('Expect "value" be none when "key" has packed kv format.');te=5,de=w.dims[1]}else{if(w.dims[1]!==u.numHeads||w.dims[3]!==Me)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');te=0,de=w.dims[2]}}else{if(h.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(h.dims[2]!==u.numHeads||h.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');te=3}if(v&&He.size(v.dims)>0){if(v.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(w&&w.dims.length===5&&w.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let De=fe+de,Le=0;if(y&&He.size(y.dims)>0){Le=8;let lA=y.dims;throw lA.length===1?lA[0]===Q?Le=1:lA[0]===3*Q+2&&(Le=3):lA.length===2&&lA[0]===Q&&lA[1]===De&&(Le=5),Le===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let Xe=!1,_e=oe;if(B&&He.size(B.dims)>0){if(B.dims.length!==3&&B.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(h.dims[0]!==B.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(B.dims.length===3){if(de!==B.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');_e=B.dims[2]}else{if(de!==B.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');_e=B.dims[1]*B.dims[3],Xe=!0}}if(y&&He.size(y.dims)>0)throw new Error("Key padding mask is not supported");if(F&&He.size(F.dims)>0){if(F.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(F.dims[0]!==Q||F.dims[1]!==u.numHeads||F.dims[2]!==ie||F.dims[3]!==De)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:Q,sequenceLength:ie,pastSequenceLength:fe,kvSequenceLength:de,totalSequenceLength:De,maxSequenceLength:ke,inputHiddenSize:0,hiddenSize:oe,vHiddenSize:_e,headSize:Me,vHeadSize:Math.floor(_e/u.numHeads),numHeads:u.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:u.maskFilterValue,maskType:Le,scale:u.scale,broadcastResPosBias:!1,passPastInKv:Xe,qkvFormat:te}},z2=n=>$e({...n}),U0=$e({perm:[0,2,1,3]}),F2=(n,u,h,w,B,v,y)=>{let F=[w,B,v],N=He.size(F),Y=[{type:12,data:N},{type:12,data:y},{type:12,data:v}];return n.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:F,dataType:u.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(N/64)},programUniforms:Y}),getShaderSource:te=>{let Q=JA("qkv_with_bias",u.dataType,F),ie=gA("qkv",u.dataType,F),oe=gA("bias",h.dataType,F);return`
  ${te.registerUniforms([{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}]).declareVariables(ie,oe,Q)}
  ${te.mainStart()}
    ${te.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;

    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];
  }`}},{inputs:[u,h],outputs:[-1]})[0]},P0=(n,u,h,w,B,v,y,F)=>{let N=v;if(y&&He.size(y.dims)>0){if(w===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return N=F2(n,v,y,u,w,h*B,F),N=N.reshape([u,w,h,B]),h===1||w===1?N:n.compute(Ma(N,U0.perm),{inputs:[N],outputs:[-1]})[0]}return v.dims.length===3&&(N=v.reshape([u,w,h,B])),h===1||w===1?N:n.compute(Ma(N,U0.perm),{inputs:[N],outputs:[-1]})[0]},Qi=(n,u)=>{let h=P2(n.inputs,u),w=n.inputs[0],B=Ha(n.inputs,1),v=Ha(n.inputs,2),y=Ha(n.inputs,3),F=Ha(n.inputs,4),N=Ha(n.inputs,5),Y=Ha(n.inputs,6),te=Ha(n.inputs,7);if(w.dims.length===5)throw new Error("Packed QKV is not implemented");if(B?.dims.length===5)throw new Error("Packed KV is not implemented");let Q=B&&v&&B.dims.length===4&&v.dims.length===4,ie=P0(n,h.batchSize,h.numHeads,h.sequenceLength,h.headSize,w,y,0);if(Q)return fs(n,ie,B,v,F,void 0,Y,te,N,h);if(!B||!v)throw new Error("key and value must be provided");let oe=P0(n,h.batchSize,h.numHeads,h.kvSequenceLength,h.headSize,B,y,h.hiddenSize),de=P0(n,h.batchSize,h.numHeads,h.kvSequenceLength,h.vHeadSize,v,y,2*h.hiddenSize);fs(n,ie,oe,de,F,void 0,Y,te,N,h)}}),FA=Pe(()=>{Dt(),St(),qr(),Rt(),S2=n=>{if(!n||n.length<1)throw new Error("too few inputs")},N2=(n,u)=>{let h=[],w=u.numOutputs;return n[1].dims[0]>0&&(n[1].getBigInt64Array().forEach(B=>h.push(Number(B))),w=h.length),$e({numOutputs:w,axis:u.axis,splitSizes:h})},O2=n=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${n}u; i += 1u ) {
    if (index < ${rt("uniforms.size_in_split_axis","i",n)}) {
        return i;
    }
    }
    return ${n}u;
}`,L2=n=>{let u=n.length,h=[];for(let w=0;w<u;++w){let B=n[w].setByIndices("indices","input[global_idx]");u===1?h.push(B):w===0?h.push(`if (output_number == ${w}u) { ${B} }`):w===u-1?h.push(`else { ${B} }`):h.push(`else if (output_number == ${w}) { ${B} }`)}return`
      fn writeBufferData(output_number: u32, indices: ${n[0].type.indices}, global_idx: u32) {
        ${h.join(`
`)}
      }`},K0=(n,u)=>{let h=n[0].dims,w=He.size(h),B=n[0].dataType,v=He.normalizeAxis(u.axis,h.length),y=new Array(u.numOutputs),F=gA("input",B,h.length),N=new Array(u.numOutputs),Y=[],te=[],Q=0,ie=[{type:12,data:w}];for(let oe=0;oe<u.numOutputs;oe++){Q+=u.splitSizes[oe],N[oe]=Q;let de=h.slice();de[v]=u.splitSizes[oe],te.push(de),y[oe]=JA(`output${oe}`,B,de.length),Y.push({dims:te[oe],dataType:n[0].dataType})}return ie.push({type:12,data:N},...KA(h,...te)),{name:"Split",shaderCache:{hint:u.cacheKey,inputDependencies:["rank"]},getShaderSource:oe=>`
  ${oe.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",N.length).declareVariables(F,...y)}
  ${O2(N.length)}
  ${L2(y)}

  ${oe.mainStart()}
    ${oe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")}

    var indices = ${F.offsetToIndices("global_idx")};
    var index = ${F.indicesGet("indices",v)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${rt("uniforms.size_in_split_axis","output_number - 1u",N.length)};
      ${F.indicesSet("indices",v,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`,getRunData:()=>({outputs:Y,dispatchGroup:{x:Math.ceil(w/64)},programUniforms:ie})}},Y2=(n,u)=>{S2(n.inputs);let h=n.inputs.length===1?u:N2(n.inputs,u);n.compute(K0(n.inputs,h),{inputs:[0]})},H2=n=>{let u=n.axis,h=n.splitSizes,w=n.numOutputs<0?h.length:n.numOutputs;if(w!==h.length)throw new Error("numOutputs and splitSizes lengh must be equal");return $e({axis:u,numOutputs:w,splitSizes:h})}}),XA=Pe(()=>{qr(),n1(),uA(),FA(),tn(),R2=(n,u)=>{if(u.doRotary)throw new Error("GroupQuerryAttention do_rotary attribute is not supported");if(u.doRotary&&n.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let h=n[0],w=n[1],B=n[2],v=n[3],y=n[4];if(u.localWindowSize!==-1)throw new Error("Local attention is not supported");if(u.softcap!==0)throw new Error("Softcap is not supported");if(u.rotaryInterleaved!==0)throw new Error("Rotary interleaved is not supported");if(u.smoothSoftmax)throw new Error("Smooth softmax is not supported");if(h.dims.length!==3&&h.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let F=h.dims[0],N=h.dims[1],Y=h.dims.length===3?h.dims[2]:u.numHeads*h.dims[4],te=N,Q=0,ie=!w||w.dims.length===0,oe=Math.floor(ie?Y/(u.numHeads+2*u.kvNumHeads):Y/u.numHeads);ie&&(Y=oe*u.numHeads);let de=v&&v.dims.length!==0,fe=y&&y.dims.length!==0;if(de&&v.dims.length===4&&v.dims[0]===F&&v.dims[1]!==u.kvNumHeads&&v.dims[2]===u.kvNumHeads&&v.dims[3]===oe)throw new Error("BSNH pastKey/pastValue is not supported");if(de&&fe){if(v.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(y.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');Q=v.dims[2]}else if(de||fe)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let ke=1;if(w&&w.dims.length>0){if(h.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(w.dims.length<3||w.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(h.dims[0]!==w.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(w.dims.length===3){if(h.dims[2]%w.dims[2]!=0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');te=w.dims[1]}else if(w.dims.length===5){if(w.dims[2]!==u.numHeads||w.dims[3]!==2||w.dims[4]!==oe)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(B)throw new Error('Expect "value" be none when "key" has packed kv format.');te=w.dims[1]}else{if(w.dims[1]!==u.numHeads||w.dims[3]!==oe)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');te=w.dims[2]}}else{if(h.dims.length!==3&&h.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(h.dims.length===5&&(h.dims[2]!==u.numHeads||h.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');ke=3}let Me=!1,De=u.kvNumHeads?oe*u.kvNumHeads:Y;if(B&&B.dims.length>0){if(B.dims.length!==3&&B.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(h.dims[0]!==B.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(B.dims.length===3){if(te!==B.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');De=B.dims[2]}else{if(te!==B.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');De=B.dims[1]*B.dims[3],Me=!0}}let Le=n.length>4?n[5]:void 0;if(Le&&Le.dims.length!==1&&Le.dims[0]!==F)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:F,sequenceLength:N,pastSequenceLength:Q,kvSequenceLength:te,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:Y,vHiddenSize:De,headSize:oe,vHeadSize:Math.floor(De/u.kvNumHeads),numHeads:u.numHeads,kvNumHeads:u.kvNumHeads,nReps:u.numHeads/u.kvNumHeads,pastPresentShareBuffer:!1,maskType:0,scale:u.scale,broadcastResPosBias:!1,passPastInKv:Me,qkvFormat:ke}},W2=$e({perm:[0,2,1,3]}),z0=(n,u,h)=>{let w=u,B=h.kvNumHeads;return u.dims.length===3&&h.kvSequenceLength!==0&&(w=u.reshape([h.batchSize,h.kvSequenceLength,B,h.headSize]),w=n.compute(Ma(w,W2.perm),{inputs:[w],outputs:[-1]})[0]),w},V2=(n,u)=>{let h=R2(n.inputs,u);if(n.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(n.inputs[1]?.dims.length===5)throw new Error("Packed KV is not implemented");let w=n.inputs[0],B=n.inputs[1]&&n.inputs[1].dims.length>0?n.inputs[1]:void 0,v=n.inputs[2]&&n.inputs[2].dims.length>0?n.inputs[2]:void 0,y=n.inputs[3]&&n.inputs[3].dims.length!==0?n.inputs[3]:void 0,F=n.inputs[4]&&n.inputs[4].dims.length!==0?n.inputs[4]:void 0,N=n.inputs.length>4?n.inputs[5]:void 0,Y=n.inputs.length>5?n.inputs[6]:void 0,te=h.kvNumHeads?h.kvNumHeads:h.numHeads,Q=$e({axis:2,numOutputs:3,splitSizes:[h.numHeads*h.headSize,te*h.headSize,te*h.headSize]}),[ie,oe,de]=B||v?[w,B,v]:n.compute(K0([w],Q),{inputs:[w],outputs:[-1,-1,-1]}),fe=P0(n,h.batchSize,h.numHeads,h.sequenceLength,h.headSize,ie,void 0,0);fs(n,fe,z0(n,oe,h),z0(n,de,h),void 0,void 0,y,F,void 0,h,N,Y)}}),WA=Pe(()=>{Dt(),St(),tn(),Rt(),Co=(n,u,h,w,B,v,y,F)=>{let N=Jt(v),Y=N===1?"f32":`vec${N}f`,te=N===1?"vec2f":`mat2x${N}f`,Q=B*y,ie=64;Q===1&&(ie=256);let oe=[B,y,v/N],de=[B,y,2],fe=[];return fe.push(...KA(oe,de)),n.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${N};${F};${ie}`,inputDependencies:["rank","type","type"]},getRunData:()=>({outputs:[{dims:de,dataType:1}],dispatchGroup:{x:Q},programUniforms:fe}),getShaderSource:ke=>{let Me=gA("x",u.dataType,3,N),De=[Me,gA("scale",h.dataType,h.dims),gA("bias",w.dataType,w.dims),JA("output",1,3,2)];return`
  var<workgroup> workgroup_shared : array<${te}, ${ie}>;
  const workgroup_size = ${ie}u;
  ${ke.declareVariables(...De)}
  ${ke.mainStart(ie)}
    let batch = workgroup_index / uniforms.x_shape[1];
    let channel = workgroup_index % uniforms.x_shape[1];
    let hight = uniforms.x_shape[2];
    // initialize workgroup memory
    var sum = ${Y}(0);
    var squared_sum = ${Y}(0);
    for (var h = local_idx; h < hight; h += workgroup_size) {
      let value = ${Y}(${Me.get("batch","channel","h")});
      sum += value;
      squared_sum += value * value;
    }
    workgroup_shared[local_idx] = ${te}(sum, squared_sum);
    workgroupBarrier();

    for (var currSize = workgroup_size >> 1;  currSize > 0; currSize = currSize >> 1) {
      if (local_idx < currSize) {
        workgroup_shared[local_idx] = workgroup_shared[local_idx] + workgroup_shared[local_idx + currSize];
      }
      workgroupBarrier();
    }
    if (local_idx == 0) {
      let sum_final = ${ir("workgroup_shared[0][0]",N)} / f32(hight * ${N});
      let squared_sum_final = ${ir("workgroup_shared[0][1]",N)} / f32(hight * ${N});

      let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${F}));
      let channel_scale = inv_std_dev * f32(scale[channel]);
      let channel_shift = f32(bias[channel]) - sum_final * channel_scale;
      output[workgroup_index] = vec2f(channel_scale, channel_shift);
    }
  }`}},{inputs:[u,h,w],outputs:[-1]})[0]},j2=(n,u,h)=>{let w=u[0].dims,B=w,v=w[0],y=w[1],F=He.sizeFromDimension(w,2),N=Jt(F),Y=He.size(B)/N,te=Co(n,u[0],u[1],u[2],v,F,y,h.epsilon),Q=[v,y,F/N],ie=[v,y];n.compute({name:"InstanceNormalization",shaderCache:{hint:`${N}`,inputDependencies:["type","none"]},getRunData:()=>({outputs:[{dims:B,dataType:u[0].dataType}],dispatchGroup:{x:Math.ceil(Y/64)},programUniforms:[{type:12,data:Y},...KA(Q,ie,Q)]}),getShaderSource:oe=>{let de=gA("x",u[0].dataType,Q.length,N),fe=gA("scale_shift",1,ie.length,2),ke=JA("output",u[0].dataType,Q.length,N),Me=[de,fe,ke];return`
  ${oe.registerUniform("output_size","u32").declareVariables(...Me)}
  ${oe.mainStart()}
  ${oe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let outputIndices = ${ke.offsetToIndices("global_idx")};
      let batch = outputIndices[0];
      let channel = outputIndices[1];
      let scale_shift = ${fe.getByIndices("vec2<u32>(batch, channel)")};
      let value = ${de.getByOffset("global_idx")} * ${ke.type.value}(scale_shift.x) + ${ke.type.value}(scale_shift.y);
      ${ke.setByOffset("global_idx","value")};
  }`}},{inputs:[u[0],te]})},F0=(n,u,h)=>{let w=u[0].dims,B=w,v=w[0],y=w[w.length-1],F=He.sizeFromDimension(w,1)/y,N=Jt(y),Y=He.size(B)/N,te=[{type:12,data:F},{type:12,data:Math.floor(y/N)}],Q=!1,ie=[0,w.length-1];for(let fe=0;fe<w.length-2;fe++)Q=Q||w[fe+1]!==1,ie.push(fe+1);Q=Q&&w[w.length-1]!==1;let oe=Q?n.compute(Ma(n.inputs[0],ie),{inputs:[n.inputs[0]],outputs:[-1]})[0]:n.inputs[0].reshape(Array.from({length:w.length},(fe,ke)=>w[ie[ke]])),de=Co(n,oe,u[1],u[2],v,F,y,h.epsilon);n.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${N}`,inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:B,dataType:u[0].dataType}],dispatchGroup:{x:Math.ceil(Y/64)},programUniforms:te}),getShaderSource:fe=>{let ke=jt(u[0].dataType),Me=N===1?"vec2f":`mat${N}x2f`,De=_e=>{let lA=_e===0?"x":"y",LA=N===1?"f32":`vec${N}f`;switch(N){case 1:return`${ke}(${LA}(scale.${lA}))`;case 2:return`vec2<${ke}>(${LA}(scale[0].${lA}, scale[1].${lA}))`;case 4:return`vec4<${ke}>(${LA}(scale[0].${lA}, scale[1].${lA}, scale[2].${lA}, scale[3].${lA}))`;default:throw new Error(`Not supported compoents ${N}`)}},Le=gA("input",u[0].dataType,u[0].dims,N),Xe=JA("output",u[0].dataType,B,N);return`
  @group(0) @binding(0) var<storage, read> input : array<${Le.type.storage}>;
  @group(0) @binding(1) var<storage, read> scale_input : array<${Me}>;
  @group(0) @binding(2) var<storage, read_write> output : array<${Xe.type.storage}>;
  struct Uniforms {H: u32, C : u32};
  @group(0) @binding(3) var<uniform> uniforms: Uniforms;

  ${fe.mainStart()}
    let current_image_number = global_idx / (uniforms.C * uniforms.H);
    let current_channel_number = global_idx % uniforms.C;

    let scale_offset = current_image_number * uniforms.C + current_channel_number;
    let scale = scale_input[scale_offset];
    output[global_idx] = fma(input[global_idx], ${De(0)}, ${De(1)});
  }`}},{inputs:[u[0],de]})},U2=(n,u)=>{u.format==="NHWC"?F0(n,n.inputs,u):j2(n,n.inputs,u)}}),_A=Pe(()=>{Dt(),St(),Rt(),K2=n=>{if(!n||n.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Z2=(n,u,h)=>{let w=u.simplified,B=n[0].dims,v=n[1],y=!w&&n[2],F=B,N=He.normalizeAxis(u.axis,B.length),Y=He.sizeToDimension(B,N),te=He.sizeFromDimension(B,N),Q=He.size(v.dims),ie=y?He.size(y.dims):0;if(Q!==te||y&&ie!==te)throw new Error(`Size of X.shape()[axis:] == ${te}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${Q} and bias size of ${ie}`);let oe=[];for(let Xe=0;Xe<B.length;++Xe)Xe<N?oe.push(B[Xe]):oe.push(1);let de=Jt(te),fe=["type","type"],ke=[{type:12,data:Y},{type:1,data:te},{type:12,data:Math.floor(te/de)},{type:1,data:u.epsilon}];y&&fe.push("type");let Me=h>1,De=h>2,Le=[{dims:F,dataType:n[0].dataType}];return Me&&Le.push({dims:oe,dataType:1}),De&&Le.push({dims:oe,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${de};${h};${w}`,inputDependencies:fe},getRunData:()=>({outputs:Le,dispatchGroup:{x:Math.ceil(Y/64)},programUniforms:ke}),getShaderSource:Xe=>{let _e=jt(n[0].dataType),lA=[gA("x",n[0].dataType,n[0].dims,de),gA("scale",v.dataType,v.dims,de)];return y&&lA.push(gA("bias",y.dataType,y.dims,de)),lA.push(JA("output",n[0].dataType,F,de)),Me&&lA.push(JA("mean_data_output",1,oe)),De&&lA.push(JA("inv_std_output",1,oe)),`
  ${Xe.registerUniforms([{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}]).declareVariables(...lA)}
  ${Xe.mainStart()}
    ${Xe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${La("f32",de)};
    var mean_square_vector = ${La("f32",de)};

    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {
      let value = ${fa(_e,de,"x[h + offset]")};
      mean_vector += value;
      mean_square_vector += value * value;
    }
    let mean = ${ir("mean_vector",de)} / uniforms.norm_size;
    let inv_std_dev = inverseSqrt(${ir("mean_square_vector",de)} / uniforms.norm_size ${w?"":"- mean * mean"} + uniforms.epsilon);

    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {
      let f32input = ${fa(_e,de,"x[j + offset]")};
      let f32scale = ${fa(_e,de,"scale[j]")};
      output[j + offset] = ${lA[0].type.value}((f32input ${w?"":"- mean"}) * inv_std_dev * f32scale
        ${y?`+ ${fa(_e,de,"bias[j]")}`:""}
      );
    }

    ${Me?"mean_data_output[global_idx] = mean":""};
    ${De?"inv_std_output[global_idx] = inv_std_dev":""};
  }`}}},X2=(n,u)=>{K2(n.inputs),n.compute(Z2(n.inputs,u,n.outputCount))}}),ft=Pe(()=>{St(),c1(),g1(),Z0=n=>{if(!n||n.length!==2)throw new Error("MatMul requires 2 inputs.");if(n[0].dims[n[0].dims.length-1]!==n[1].dims[n[1].dims.length-2])throw new Error("shared dimension does not match.")},q2=n=>{Z0(n.inputs);let u=NA.calcShape(n.inputs[0].dims,n.inputs[1].dims,!0);if(!u)throw new Error("Can't use matmul on the given tensors");let h=u[u.length-1],w=n.inputs[0].dims[n.inputs[0].dims.length-1];if(h<8&&w<8)n.compute(Mi(n.inputs,{activation:""},u));else{let B=u[u.length-2],v=He.size(n.inputs[0].dims.slice(0,-2)),y=He.size(n.inputs[1].dims.slice(0,-2));if(v!==1&&B===1&&y===1){let F=[1,v,h],N=[n.inputs[0].reshape([1,v,w]),n.inputs[1].reshape([1,w,h])];n.compute(A(N,{activation:""},u,F),{inputs:N})}else n.compute(A(n.inputs,{activation:""},u))}}}),gt=Pe(()=>{Dt(),St(),qr(),Rt(),J2=(n,u)=>{if(n.length<3||n.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let h=n[0],w=h.dims.length;if(h.dims[w-1]!==u.k)throw new Error("The last dim of input shape does not match the k value");let B=Math.floor((u.k+u.blockSize-1)/u.blockSize),v=u.blockSize/8*u.bits,y=n[1];if(!He.areEqual(y.dims,[u.n,B,v]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let F=n[2].dims;if(He.size(F)!==u.n*B)throw new Error("scales input size error.");if(n.length===4){let N=n[3].dims,Y=u.bits>4?u.n*B:u.n*Math.floor((B+1)/2);if(He.size(N)!==Y)throw new Error("zeroPoints input size error.")}},_2=(n,u)=>{let h=n[0].dims,w=h.length,B=h[w-2],v=u.k,y=u.n,F=h.slice(0,w-2),N=He.size(F),Y=n[1].dims[2]/4,te=n[0].dataType,Q=Jt(u.k),ie=Jt(Y),oe=Jt(y),de=F.concat([B,y]),fe=B>1&&y/oe%2==0?2:1,ke=He.size(de)/oe/fe,Me=64,De=[],Le=[N,B,v/Q],Xe=He.convertShape(n[1].dims).slice();Xe.splice(-1,1,Y/ie),De.push(...KA(Le)),De.push(...KA(Xe)),De.push(...KA(n[2].dims)),n.length===4&&De.push(...KA(He.convertShape(n[3].dims)));let _e=[N,B,y/oe];return De.push(...KA(_e)),{name:"MatMulNBits",shaderCache:{hint:`${u.blockSize};${u.bits};${Q};${ie};${oe};${fe};64`,inputDependencies:Array(n.length).fill("rank")},getRunData:()=>({outputs:[{dims:de,dataType:te}],dispatchGroup:{x:ke},programUniforms:De}),getShaderSource:lA=>{let LA=Le.length,st=gA("a",n[0].dataType,LA,Q),ht=gA("b",12,Xe.length,ie),pt=gA("scales",n[2].dataType,n[2].dims.length),mt=[st,ht,pt],vt=n.length===4?gA("zero_points",12,n[3].dims.length):void 0;vt&&mt.push(vt);let tt=_e.length,lt=JA("output",n[0].dataType,tt,oe),wA=jt(n[0].dataType),UA=(()=>{switch(Q){case 1:return`array<${wA}, 8>`;case 2:return`mat4x2<${wA}>`;case 4:return`mat2x4<${wA}>`;default:throw new Error(`${Q}-component is not supported.`)}})();return`
        var<workgroup> workgroup_shared: array<${lt.type.value}, ${fe*Me}>;
        ${lA.declareVariables(...mt,lt)}
        ${lA.mainStart([Me,1,1])}
          let output_indices = ${lt.offsetToIndices(`(global_idx / 64) * ${fe}`)};
          let col = output_indices[2];
          let row = output_indices[1];
          let batch = output_indices[0];
          let nBlocksPerCol = uniforms.b_shape[1];

          for (var block = local_id.x; block < nBlocksPerCol; block += 64) {
            //process one block
            var word_offset: u32 = block * ${u.blockSize/Q};
            ${(()=>{let At=`
            var col_index = col * ${oe};
            ${vt?`
            let zero_point_bytes_per_col = (nBlocksPerCol + 1) / 2;
            var zero_point_byte_count: u32;
            var zero_point_word_index: u32;
            var zero_point_byte_offset: u32;
            let zero_point_nibble_offset: u32 = block & 0x1u;
            var zero_point_bits_offset: u32;
            var zero_point_word: u32;`:`
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${wA}(8);`}
            `;for(let yt=0;yt<oe*fe;yt++)At+=`
            let scale${yt} = ${pt.getByOffset("col_index * nBlocksPerCol + block")};
            ${vt?`
            zero_point_byte_count = col_index * zero_point_bytes_per_col + (block >> 0x1u);
            zero_point_word_index = zero_point_byte_count >> 0x2u;
            zero_point_byte_offset = zero_point_byte_count & 0x3u;
            zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
            zero_point_word = ${vt.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
            let zero_point${yt} = ${wA}((zero_point_word) & 0xFu);`:""}
            col_index += 1;`;return At})()}
            for (var word: u32 = 0; word < ${Y}; word += ${ie}) {
              ${(()=>{let At=`col_index = col * ${oe};`;for(let yt=0;yt<oe*fe;yt++)At+=`
            let b${yt}_data = ${ht.getByIndices(`${ht.type.indices}(col_index, block, word)`)};
            col_index += 1;`;return At+=`
            var b_value: u32;
            let b_mask: u32 = 0x0F0F0F0Fu;
            var b_value_lower: vec4<u32>;
            var b_value_upper: vec4<u32>;
            var b_quantized_values: ${UA};
            var b_dequantized_values: ${UA};`,At})()}
              for (var i: u32 = 0; i < ${ie}; i++) {
                ${(()=>{let At=`
          // reuse a data
            var input_offset = ${st.indicesToOffset(`${st.type.indices}(batch, row, word_offset)`)};
            var a_data: ${UA};
            for (var j: u32 = 0; j < ${8/Q}; j++) {
              a_data[j] = ${st.getByOffset("input_offset")};
              input_offset++;
            }
          `;for(let yt=0;yt<oe*fe;yt++)At+=`
            b_value = ${ie===1?`b${yt}_data`:`b${yt}_data[i]`};
            b_value_lower = unpack4xU8(b_value & b_mask);
            b_value_upper = unpack4xU8((b_value >> 4) & b_mask);
            b_quantized_values = ${UA}(${Array.from({length:4},(gr,la)=>`${wA}(b_value_lower[${la}]), ${wA}(b_value_upper[${la}])`).join(", ")});
            b_dequantized_values = ${Q===1?`${UA}(${Array.from({length:8},(gr,la)=>`(b_quantized_values[${la}] - ${vt?`zero_point${yt}`:"zero_point"}) * scale${yt}`).join(", ")});`:`(b_quantized_values - ${UA}(${Array(8).fill(vt?`zero_point${yt}`:"zero_point").join(",")})) * scale${yt};`};
            workgroup_shared[local_id.x * ${fe} + ${Math.floor(yt/oe)}]${oe>1?`[${yt%oe}]`:""} += ${Array.from({length:8/Q},(gr,la)=>Q===1?`a_data[${la}] * b_dequantized_values[${la}]`:`dot(a_data[${la}], b_dequantized_values[${la}])`).join(" + ")};
          `;return At})()}
                word_offset += ${8/Q};
              }
            }
          }
          workgroupBarrier();

          if (local_id.x < ${fe}) {
            var output_value: ${lt.type.value} = ${lt.type.value}(0);
            var workgroup_shared_offset: u32 = local_id.x;
            for (var b: u32 = 0u; b < 64u; b++) {
              output_value += workgroup_shared[workgroup_shared_offset];
              workgroup_shared_offset += ${fe};
            }
            ${lt.setByIndices(`${lt.type.indices}(batch, row, col + local_id.x)`,"output_value")};
          }
        }`}}},B1=(n,u)=>{let h=n[0].dims,w=h.length,B=h[w-2],v=u.k,y=u.n,F=h.slice(0,w-2),N=He.size(F),Y=n[1].dims[2]/4,te=n[0].dataType,Q=Jt(u.k),ie=Jt(Y),oe=F.concat([B,y]),de=y%8==0?8:y%4==0?4:1,fe=128/de,ke=fe*ie*8,Me=ke/Q,De=ke/u.blockSize,Le=He.size(oe)/de,Xe=[],_e=[N,B,v/Q],lA=He.convertShape(n[1].dims).slice();lA.splice(-1,1,Y/ie),Xe.push(...KA(_e)),Xe.push(...KA(lA)),Xe.push(...KA(n[2].dims)),n.length===4&&Xe.push(...KA(He.convertShape(n[3].dims)));let LA=[N,B,y];return Xe.push(...KA(LA)),{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${u.blockSize};${Q};${ie};${fe};${de}`,inputDependencies:Array(n.length).fill("rank")},getRunData:()=>({outputs:[{dims:oe,dataType:te}],dispatchGroup:{x:Le},programUniforms:Xe}),getShaderSource:st=>{let ht=_e.length,pt=gA("a",n[0].dataType,ht,Q),mt=gA("b",12,lA.length,ie),vt=gA("scales",n[2].dataType,n[2].dims.length),tt=[pt,mt,vt],lt=n.length===4?gA("zero_points",12,n[3].dims.length):void 0;lt&&tt.push(lt);let wA=LA.length,UA=JA("output",n[0].dataType,wA),At=jt(n[0].dataType);return`
        var<workgroup> sub_a: array<${pt.type.value}, ${Me}>;
        var<workgroup> inter_results: array<array<${UA.type.value}, ${fe}>, ${de}>;
        ${st.declareVariables(...tt,UA)}
        ${st.mainStart([fe,de,1])}
          let output_indices = ${UA.offsetToIndices(`workgroup_index * ${de}`)};
          let col = output_indices[2];
          let row = output_indices[1];
          let batch = output_indices[0];
          let n_blocks_per_col = uniforms.b_shape[1];
          let num_tiles =  (n_blocks_per_col - 1) / ${De} + 1;

          // Loop over shared dimension.
          for (var tile: u32 = 0; tile < num_tiles; tile += 1) {
            let a_col_start = tile * ${Me};
            // load one tile A data into shared memory.
            for (var a_offset = local_idx; a_offset < ${Me}; a_offset += 128)
            {
              let a_col = a_col_start + a_offset;
              if (a_col < uniforms.a_shape[2])
              {
                sub_a[a_offset] = ${pt.getByIndices(`${pt.type.indices}(batch, row, a_col)`)};
              } else {
                sub_a[a_offset] = ${pt.type.value}(0);
              }
            }
            workgroupBarrier();

            // each thread process one block
            let b_row = col + local_id.y;
            let block = tile * ${De} + local_id.x;
            ${lt?`
            let zero_point_bytes_per_col = (n_blocks_per_col + 1) / 2;
            let zero_point_byte_count = b_row * zero_point_bytes_per_col + (block >> 0x1u);
            let zero_point_word_index = zero_point_byte_count >> 0x2u;
            let zero_point_byte_offset = zero_point_byte_count & 0x3u;
            let zero_point_nibble_offset: u32 = block & 0x1u;
            let zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
            let zero_point_word = ${lt.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
            let zero_point = ${At}((zero_point_word) & 0xFu);`:`
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${At}(8);`}
            let scale = ${vt.getByOffset("b_row * n_blocks_per_col + block")};
            let b_data = ${mt.getByIndices(`${mt.type.indices}(b_row, block, 0)`)};
            var word_offset = local_id.x * ${u.blockSize/Q};
            for (var i: u32 = 0; i < ${ie}; i++) {
              ${(()=>{switch(Q){case 1:return`
          let a_data0 = vec4<${At}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]);
          let a_data1 = vec4<${At}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return`
          let a_data0 = vec4<${At}>(sub_a[word_offset], sub_a[word_offset + 1]);
          let a_data1 = vec4<${At}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return`
          let a_data0 = sub_a[word_offset];
          let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${Q}-component is not supported.`)}})()}
              let b_value = ${ie===1?"b_data":"b_data[i]"};
              let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu);
              let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu);
              let b_quantized_values = mat2x4<${At}>(${Array.from({length:4},(yt,gr)=>`${At}(b_value_lower[${gr}]), ${At}(b_value_upper[${gr}])`).join(", ")});
              let b_dequantized_values = (b_quantized_values - mat2x4<${At}>(${Array(8).fill("zero_point").join(",")})) * scale;
              inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(yt,gr)=>`dot(a_data${gr}, b_dequantized_values[${gr}])`).join(" + ")};
              word_offset += ${8/Q};
            }
            workgroupBarrier();
          }

          if (local_idx < ${de}) {
            var output_value: ${UA.type.value} = ${UA.type.value}(0);
            for (var b = 0u; b < ${fe}; b++) {
              output_value += inter_results[local_idx][b];
            }
            if (col + local_idx < uniforms.output_shape[2])
            {
              ${UA.setByIndices(`${UA.type.indices}(batch, row, col + local_idx)`,"output_value")}
            }
          }
        }`}}},$2=(n,u)=>{J2(n.inputs,u),u.blockSize===32&&n.adapterInfo.isVendor("intel")&&n.adapterInfo.isArchitecture("gen-12lp")?n.compute(B1(n.inputs,u)):n.compute(_2(n.inputs,u))},el=n=>$e(n)}),zt=Pe(()=>{Dt(),St(),Rt(),Al=n=>{if(!n||n.length<1)throw new Error("Too few inputs");if(n[0].dataType!==1&&n[0].dataType!==10)throw new Error("Input type must be float or float16.");if(n.length>=2){let u=2*n[0].dims.length===n[1].dims[0];if(n.length===4&&(u=2*n[3].dims[0]===n[1].dims[0]),!u)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},Kn=(n,u,h)=>{let w="";for(let B=u-1;B>=0;--B)w+=`
            k = i32(${n.indicesGet("indices",B)}) - ${rt("uniforms.pads",B,h)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${rt("uniforms.x_shape",B,u)})) {
              break;
            }
            offset += k * i32(${rt("uniforms.x_strides",B,u)});
        `;return`
          value = ${n.type.value}(uniforms.constant_value);
          for (var i = 0; i < 1; i++) {
            var offset = 0;
            var k = 0;
            ${w}
            value = x[offset];
          }
      `},tl=(n,u,h)=>{let w="";for(let B=u-1;B>=0;--B)w+=`
                k = i32(${n.indicesGet("indices",B)}) - ${rt("uniforms.pads",B,h)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${rt("uniforms.x_shape",B,u)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${rt("uniforms.x_shape",B,u)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${rt("uniforms.x_strides",B,u)});
            `;return`
              var offset = 0;
              var k = 0;
              ${w}
              value = x[offset];
          `},rl=(n,u,h)=>{let w="";for(let B=u-1;B>=0;--B)w+=`
                k = i32(${n.indicesGet("indices",B)}) - ${rt("uniforms.pads",B,h)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${rt("uniforms.x_shape",B,u)})) {
                  k = i32(${rt("uniforms.x_shape",B,u)}) - 1;
                }
                offset += k * i32(${rt("uniforms.x_strides",B,u)});
            `;return`
              var offset = 0;
              var k = 0;
              ${w}
              value = x[offset];
          `},al=(n,u,h)=>{let w="";for(let B=u-1;B>=0;--B)w+=`
                k = i32(${n.indicesGet("indices",B)}) - ${rt("uniforms.pads",B,h)};
                if (k < 0)  {
                  k += i32(${rt("uniforms.x_shape",B,u)}]);
                }
                if (k >= i32(${rt("uniforms.x_shape",B,u)})) {
                  k -= i32(${rt("uniforms.x_shape",B,u)});
                }
                offset += k * i32(${rt("uniforms.x_strides",B,u)});
            `;return`
              var offset = 0;
              var k = 0;
              ${w}
              value = x[offset];
          `},sl=(n,u,h)=>{switch(h.mode){case 0:return Kn(n,u,h.pads.length);case 1:return tl(n,u,h.pads.length);case 2:return rl(n,u,h.pads.length);case 3:return al(n,u,h.pads.length);default:throw new Error("Invalid mode")}},il=(n,u)=>{let h=He.padShape(n[0].dims.slice(),u.pads),w=n[0].dims,B=[{type:12,data:He.size(h)},{type:6,data:u.pads}],v=n.length>=3&&n[2].data;return u.mode===0&&B.push({type:v?n[2].dataType:1,data:u.value}),B.push(...KA(n[0].dims,h)),{name:"Pad",shaderCache:{hint:`${u.mode}${v}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:h,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(He.size(h)/64)},programUniforms:B}),getShaderSource:y=>{let F=JA("output",n[0].dataType,h.length),N=gA("x",n[0].dataType,w.length),Y=N.type.value,te=sl(F,w.length,u),Q=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:u.pads.length}];return u.mode===0&&Q.push({name:"constant_value",type:v?Y:"f32"}),`
            ${y.registerUniforms(Q).declareVariables(N,F)}
            ${y.mainStart()}
            ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

            let indices = ${F.offsetToIndices("global_idx")};

            var value = ${Y}(0);
            ${te}
            output[global_idx] = value;
        }`}}},nl=(n,u)=>{if(n.length>1){let h=n[1].getBigInt64Array(),w=n.length>=3&&n[2].data?n[2].dataType===10?n[2].getUint16Array()[0]:n[2].getFloat32Array()[0]:0,B=n[0].dims.length,v=new Int32Array(2*B).fill(0);if(n.length>=4){let F=n[3].getBigInt64Array();for(let N=0;N<F.length;N++)v[Number(F[N])]=Number(h[N]),v[Number(F[N])+B]=Number(h[N+F.length])}else h.forEach((F,N)=>v[Number(N)]=Number(F));let y=[];return v.forEach(F=>y.push(F)),{mode:u.mode,value:w,pads:y}}return u},ol=(n,u)=>{Al(n.inputs);let h=nl(n.inputs,u);n.compute(il(n.inputs,h),{inputs:[0]})}}),Qt=Pe(()=>{dA(),Dt(),St(),Rt(),en=n=>{if(X.webgpu.validateInputContent&&(!n||n.length!==1))throw new Error("Pool ops requires 1 input.")},ll=(n,u,h)=>{let w=u.format==="NHWC",B=n.dims.slice();w&&B.splice(1,0,B.pop());let v=Object.hasOwnProperty.call(u,"dilations"),y=u.kernelShape.slice(),F=u.strides.slice(),N=v?u.dilations.slice():[],Y=u.pads.slice();VA.adjustPoolAttributes(h,B,y,F,N,Y);let te=VA.computePoolOutputShape(h,B,F,N,y,Y,u.autoPad),Q=Object.assign({},u);v?Object.assign(Q,{kernelShape:y,strides:F,pads:Y,dilations:N,cacheKey:u.cacheKey}):Object.assign(Q,{kernelShape:y,strides:F,pads:Y,cacheKey:u.cacheKey});let ie=te.slice();return ie.push(ie.splice(1,1)[0]),[Q,w?ie:te]},bo=(n,u)=>{let h=u.format==="NHWC",w=[{type:12,data:He.size(n)},{type:12,data:He.size(u.kernelShape)}],B=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(u.kernelShape.length<=2){let v=u.kernelShape[u.kernelShape.length-1],y=u.strides[u.strides.length-1],F=u.pads[u.pads.length/2-1],N=u.pads[u.pads.length-1],Y=!!(F+N);w.push({type:12,data:v},{type:12,data:y},{type:12,data:F},{type:12,data:N}),B.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let te=!1;if(u.kernelShape.length===2){let Q=u.kernelShape[u.kernelShape.length-2],ie=u.strides[u.strides.length-2],oe=u.pads[u.pads.length/2-2],de=u.pads[u.pads.length-2];te=!!(oe+de),w.push({type:12,data:Q},{type:12,data:ie},{type:12,data:oe},{type:12,data:de}),B.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[w,B,!0,Y,te]}{if(h)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let v=He.computeStrides(u.kernelShape);w.push({type:12,data:v},{type:12,data:u.pads},{type:12,data:u.strides}),B.push({name:"kernelStrides",type:"u32",length:v.length},{name:"pads",type:"u32",length:u.pads.length},{name:"strides",type:"u32",length:u.strides.length});let y=u.pads.reduce((F,N)=>F+N);return[w,B,!!y,!1,!1]}},Io=(n,u,h,w,B,v,y,F,N,Y,te,Q)=>{let ie=B.format==="NHWC",oe=u.type.value,de=JA("output",u.type.tensor,w);if(B.kernelShape.length<=2){let fe="",ke="",Me="",De=h-(ie?2:1);if(fe=te?`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${De}] = indices[${De}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${De}] < 0 || xIndices[${De}]
                      >= uniforms.x_shape[${De}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${u.indicesToOffset("xIndices")}];
                  ${v}
                }`:`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${De}] = indices[${De}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${u.indicesToOffset("xIndices")}];
                  ${v}
                }`,B.kernelShape.length===2){let Le=h-(ie?3:2);ke=Q?`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${Le}] = indices[${Le}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${Le}] < 0 || xIndices[${Le}] >= uniforms.x_shape[${Le}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${Le}] = indices[${Le}] * uniforms.sh - uniforms.phStart + j;
                `,Me=`
              }
            `}return`
            ${n.registerUniforms(N).declareVariables(u,de)}

            ${n.mainStart()}
              ${n.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

              let indices = ${de.offsetToIndices("global_idx")};
              var xIndices = ${de.offsetToIndices("global_idx")};

              var value = ${oe}(${F});
              var pad = 0;
              ${ke}
              ${fe}
              ${Me}
              ${y}

              output[global_idx] = value;
            }`}{if(ie)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let fe=B.kernelShape.length,ke=B.pads.length,Me="";return Me=Y?`
                if (xIndices[j] >= uniforms.x_shape[j]) {
                  pad++;
                  isPad = true;
                  break;
                }
              }
              if (!isPad) {
                let x_val = x[${u.indicesToOffset("xIndices")}];
                ${v}
              }`:`
              }
              let x_val = x[${u.indicesToOffset("xIndices")}];
              ${v}
            `,`
            ${n.registerUniforms(N).declareVariables(u,de)}

            ${n.mainStart()}
              ${n.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
              let indices = ${de.offsetToIndices("global_idx")};
              var xIndices = ${de.offsetToIndices("global_idx")};

              var offsets: array<u32, ${fe}>;

              var value = ${oe}(${F});
              var pad = 0;
              var isPad = false;

              for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {
                var offset = i;
                for (var j = 0u; j < ${fe-1}u; j++) {
                  offsets[j] = offset / ${rt("uniforms.kernelStrides","j",fe)};
                  offset -= offsets[j] * ${rt("uniforms.kernelStrides","j",fe)};
                }
                offsets[${fe-1}] = offset;

                isPad = false;
                for (var j = ${h-fe}u; j < ${h}u; j++) {
                  xIndices[j] = indices[j] * ${rt("uniforms.strides",`j - ${h-fe}u`,fe)}
                    + offsets[j - ${h-fe}u] - ${rt("uniforms.pads","j - 2u",ke)};
                  ${Me}
              }
              ${y}

              output[global_idx] = value;
            }`}},wo=n=>`${n.format};${n.ceilMode};${n.autoPad};${n.kernelShape.length}`,cl=n=>`${wo(n)};${n.countIncludePad}`,gl=n=>`${wo(n)};${n.storageOrder};${n.dilations}`,X0=n=>({format:n.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][n.auto_pad],ceilMode:n.ceil_mode,kernelShape:n.kernel_shape,strides:n.strides,pads:n.pads}),ko=(n,u,h,w)=>{let[B,v]=ll(u,w,h),y=gA("x",u.dataType,u.dims.length),F=y.type.value,N="";B.countIncludePad?N+=`value /= ${F}(uniforms.kernelSize);`:N+=`value /= ${F}(i32(uniforms.kernelSize) - pad);`;let[Y,te,Q,ie,oe]=bo(v,B);return Y.push(...KA(u.dims,v)),{name:n,shaderCache:{hint:`${w.cacheKey};${Q};${ie};${oe}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:v,dataType:u.dataType}],dispatchGroup:{x:Math.ceil(He.size(v)/64)},programUniforms:Y}),getShaderSource:de=>Io(de,y,u.dims.length,v.length,B,"value += x_val;",N,0,te,Q,ie,oe)}},ul=n=>{let u=n.count_include_pad!==0,h=X0(n);if(h.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let w={countIncludePad:u,...h,cacheKey:""};return{...w,cacheKey:cl(w)}},xo=(n,u)=>{en(n.inputs),n.compute(ko("AveragePool",n.inputs[0],!1,u))},Bo={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},dl=n=>{let u=n.format;return{format:u,...Bo,cacheKey:u}},vo=(n,u)=>{en(n.inputs),n.compute(ko("GlobalAveragePool",n.inputs[0],!0,u))},yo=(n,u,h,w)=>{let[B,v]=ll(u,w,h),y=gA("x",u.dataType,u.dims.length),[F,N,Y,te,Q]=bo(v,B);return F.push(...KA(u.dims,v)),{name:n,shaderCache:{hint:`${w.cacheKey};${Y};${te};${Q}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:v,dataType:u.dataType}],dispatchGroup:{x:Math.ceil(He.size(v)/64)},programUniforms:F}),getShaderSource:ie=>Io(ie,y,u.dims.length,v.length,B,`
      value = max(x_val, value);
    `,"",u.dataType===10?-65504:-1e5,N,Y,te,Q)}},fl=(n,u)=>{en(n.inputs),n.compute(yo("MaxPool",n.inputs[0],!1,u))},q0=n=>{let u=n.storage_order,h=n.dilations,w=X0(n);if(u!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(w.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let B={storageOrder:u,dilations:h,...w,cacheKey:""};return{...B,cacheKey:gl(B)}},pl=n=>{let u=n.format;return{format:u,...Bo,cacheKey:u}},hl=(n,u)=>{en(n.inputs),n.compute(yo("GlobalMaxPool",n.inputs[0],!0,u))}}),or=Pe(()=>{Dt(),St(),qr(),Rt(),ml=(n,u)=>{if(n.length<2||n.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(n.length===3&&n[1].dims===n[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(n.length===3&&n[0].dataType!==n[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(n[0].dataType===6&&n.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(n[1].dims.length!==0&&n[1].dims.length!==1&&n[1].dims.length!==n[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(n.length>2){if(n[0].dataType!==n[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(n[1].dims.length!==n[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!n[1].dims.map((h,w)=>h===n[2].dims[w]).reduce((h,w)=>h&&w,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(u.blockSize>0){if(n[1].dims.length===0||n[1].dims.length===1&&n[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!n[1].dims.map((B,v)=>v===u.axis||B===n[0].dims[v]).reduce((B,v)=>B&&v,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(n[1].dims.length!==n[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let h=n[0].dims[u.axis],w=n[1].dims[u.axis];if(u.blockSize<Math.ceil(h/w)||u.blockSize>Math.ceil(h/(w-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},Cl=(n,u)=>{let h=He.normalizeAxis(u.axis,n[0].dims.length),w=n[0].dataType,B=w===3,v=n[0].dims,y=n[1].dataType,F=He.size(v),N=w===3||w===2,Y=N?[Math.ceil(He.size(n[0].dims)/4)]:n[0].dims,te=n[1].dims,Q=n.length>2?n[2]:void 0,ie=Q?N?[Math.ceil(He.size(Q.dims)/4)]:Q.dims:void 0,oe=te.length===0||te.length===1&&te[0]===1,de=oe===!1&&te.length===1,fe=Jt(F),ke=oe&&(!N||fe===4),Me=ke?fe:1,De=ke&&!N?fe:1,Le=gA("input",N?12:w,Y.length,De),Xe=gA("scale",y,te.length),_e=Q?gA("zero_point",N?12:w,ie.length):void 0,lA=JA("output",y,v.length,Me),LA=[Le,Xe];_e&&LA.push(_e);let st=[Y,te];Q&&st.push(ie);let ht=[{type:12,data:F/Me},{type:12,data:h},{type:12,data:u.blockSize},...KA(...st,v)];return{name:"DequantizeLinear",shaderCache:{hint:u.cacheKey,inputDependencies:_e?["rank","rank","rank"]:["rank","rank"]},getShaderSource:pt=>`
      ${pt.registerUniforms([{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}]).declareVariables(...LA,lA)}
      ${pt.mainStart()}
          ${pt.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let output_indices = ${lA.offsetToIndices("global_idx")};

          // Set input x
          ${N?`
            let input = ${Le.getByOffset("global_idx / 4")};
            let x_vec = ${B?"unpack4xI8(input)":"unpack4xU8(input)"};
            let x_value = ${Me===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${Le.getByOffset("global_idx")};`};

          // Set scale input
          ${oe?`let scale_value= ${Xe.getByOffset("0")}`:de?`
            let scale_index = ${lA.indicesGet("output_indices","uniforms.axis")};
            let scale_value= ${Xe.getByOffset("scale_index")};`:`
            var scale_indices: ${Xe.type.indices} = output_indices;
            let index = ${Xe.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size;
            ${Xe.indicesSet("scale_indices","uniforms.axis","index")};
            let scale_value= ${Xe.getByIndices("scale_indices")};`};

          // Set zero-point input
          ${_e?oe?N?`
                let zero_point_input = ${_e.getByOffset("0")};
                let zero_point_vec =  ${B?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${_e.getByOffset("0")}`:de?N?`
                let zero_point_index = ${lA.indicesGet("output_indices","uniforms.axis")};
                let zero_point_input = ${_e.getByOffset("zero_point_index / 4")};
                let zero_point_vec =  ${B?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_index % 4]`:`
                let zero_point_index = ${lA.indicesGet("output_indices","uniforms.axis")};
                let zero_point_value = ${_e.getByOffset("zero_point_index")};`:N?`
                let zero_point_offset = ${Xe.indicesToOffset("scale_indices")};
                let zero_point_input = ${_e.getByOffset("zero_point_offset / 4")};
                let zero_point_vec = ${B?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${_e.getByIndices("scale_indices")};`:`let zero_point_value = ${N?B?"i32":"u32":Le.type.value}(0);`};
      // Compute and write output
      ${lA.setByOffset("global_idx",`${lA.type.value}(x_value - zero_point_value) * scale_value`)};
      }`,getRunData:()=>({outputs:[{dims:v,dataType:y}],dispatchGroup:{x:Math.ceil(F/Me/64),y:1,z:1},programUniforms:ht})}},Eo=(n,u)=>{ml(n.inputs,u),n.compute(Cl(n.inputs,u))},J0=n=>$e({axis:n.axis,blockSize:n.blockSize})}),Bt=Pe(()=>{dA(),Dt(),Rt(),v1=(n,u,h)=>{if(n===u||n<u&&h<0||n>u&&h>0)throw new Error("Range these inputs' contents are invalid.")},bl=(n,u,h,w)=>{let B=Math.abs(Math.ceil((u-n)/h)),v=[B],y=B,F=[{type:12,data:y},{type:w,data:n},{type:w,data:h},...KA(v)];return{name:"Range",shaderCache:{hint:`${w}`},getShaderSource:N=>{let Y=JA("output",w,v.length),te=Y.type.value,Q=[{name:"outputSize",type:"u32"},{name:"start",type:te},{name:"delta",type:te}];return`
        ${N.registerUniforms(Q).declareVariables(Y)}
        ${N.mainStart()}
        ${N.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${te}(global_idx) * uniforms.delta;
      }`},getRunData:()=>({outputs:[{dims:v,dataType:w}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:F})}},Il=n=>{let u=0,h=0,w=0;n.inputs[0].dataType===6?(u=n.inputs[0].getInt32Array()[0],h=n.inputs[1].getInt32Array()[0],w=n.inputs[2].getInt32Array()[0]):n.inputs[0].dataType===1&&(u=n.inputs[0].getFloat32Array()[0],h=n.inputs[1].getFloat32Array()[0],w=n.inputs[2].getFloat32Array()[0]),X.webgpu.validateInputContent&&v1(u,h,w),n.compute(bl(u,h,w,n.inputs[0].dataType),{inputs:[]})}}),Ot=Pe(()=>{Dt(),St(),qr(),Rt(),Mo=(n,u,h,w)=>{if(n!=="none"&&w!=="i32"&&w!=="u32"&&w!=="f32")throw new Error(`Input ${w} is not supported with reduction ${n}.`);let B=`{
                var oldValue = 0;
                loop {
                  let newValueF32 =`,v=`;
                  let newValue = bitcast<i32>(newValueF32);
                  let res = atomicCompareExchangeWeak(&${u}, oldValue, newValue);
                  if res.exchanged {
                    break;
                  }
                  oldValue = res.old_value;
                }
              }`;switch(n){case"none":return`${u}=${h};`;case"add":return w==="i32"||w==="u32"?`atomicAdd(&${u}, bitcast<${w}>(${h}));`:`
              ${B}bitcast<${w}>(oldValue) + (${h})${v}`;case"max":return w==="i32"||w==="u32"?`atomicMax(&${u}, bitcast<${w}>(${h}));`:`
                ${B}max(bitcast<f32>(oldValue), (${h}))${v}`;case"min":return w==="i32"||w==="u32"?`atomicMin(&${u}, bitcast<${w}>(${h}));`:`${B}min(bitcast<${w}>(oldValue), (${h}))${v}`;case"mul":return`${B}(bitcast<${w}>(oldValue) * (${h}))${v}`;default:throw new Error(`Reduction ${n} is not supported.`)}},wl=(n,u)=>{let h=n[0].dims,w=n[1].dims,B=h,v=Math.ceil(He.size(w)/1),y=w[w.length-1],F=He.sizeFromDimension(h,y),N=[{type:12,data:v},{type:12,data:y},{type:12,data:F},...KA(n[1].dims,n[2].dims,B)];return{name:"ScatterND",shaderCache:{hint:`${u.cacheKey}_${u.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:B,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(v/64)},programUniforms:N}),getShaderSource:Y=>{let te=gA("indices",n[1].dataType,n[1].dims.length),Q=gA("updates",n[2].dataType,n[2].dims.length,1),ie=u.reduction!=="none"&&u.reduction!==""?pa("output",n[0].dataType,B.length):JA("output",n[0].dataType,B.length,1);return`
      ${Y.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(te,Q,ie)}
      ${Y.mainStart()}
        ${Y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
  var data_offset = 0u;
  let indices_start = uniforms.last_index_dimension * global_idx;
  let indices_end = indices_start + uniforms.last_index_dimension;
  for (var i = indices_start; i < indices_end; i++) {
    var index = i32(indices[i].x);
    ${n[0].dims.length===1?`
    let element_count_dim = uniforms.output_strides;
    let dim_value = uniforms.output_shape;`:`
    let element_count_dim = uniforms.output_strides[i - indices_start];
    let dim_value = uniforms.output_shape[i - indices_start + uniforms.last_index_dimension];`}
    if (index >= 0) {
      if (index >= i32(dim_value)) {
        index = i32(dim_value - 1);
      }
    } else {
      if (index < -i32(dim_value)) {
        index = 0;
      } else {
        index += i32(dim_value);
      }
    }
    data_offset += u32((u32(index) * element_count_dim));
  }

  for (var i = 0u; i < uniforms.num_updates_elements; i++) {
    let value = updates[uniforms.num_updates_elements * global_idx + i];
    ${Mo(u.reduction,"output[data_offset + i]","value",ie.type.value)}
  }

      }`}}},kl=n=>$e({reduction:n.reduction}),Go=(n,u)=>{n.compute(wl(n.inputs,u),{inputs:[n.inputs[1],n.inputs[2]],outputs:[]})}}),Ir=Pe(()=>{Dt(),St(),qr(),Rt(),xl=(n,u)=>{if(n.every(h=>h>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),n.length>0){if(u.mode==="linear"){if(!(n.length===2||n.length===3||n.length===4&&n[0]===1&&n[1]===1||n.length===4&&n[0]===1&&n[3]===1||n.length===5&&n[0]===1&&n[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and
            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(u.mode==="cubic"&&!(n.length===2||n.length===4&&n[0]===1&&n[1]===1||n.length===4&&n[0]===1&&n[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},Bl=(n,u,h)=>{u.every(B=>B>=0&&B<h||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let w=new Array(h).fill(1);return u.forEach((B,v)=>w[B]=n[v]),w},Do=(n,u,h,w,B,v)=>{let[y,F,N]=h>10?[1,2,3]:[-1,n.length>1?1:-1,-1],Y=n[0].dims.length;if(y>0&&n.length>y&&n[y].dims.length>0)n[y].getFloat32Array().forEach(te=>v.push(te));else if(u.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(F>0&&n.length>F&&n[F].dims.length===1&&n[F].dims[0]>0){if(n[F].getFloat32Array().forEach(te=>w.push(te)),w.length!==0&&w.length!==Y&&h>=18&&w.length!==u.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");xl(w,u),u.axes.length>0&&Bl(w,u.axes,Y).forEach((te,Q)=>w[Q]=te)}if(N>0&&n.length>N&&n[N].dims.length===1&&n[N].dims[0]>0&&(n[N].getBigInt64Array().forEach(te=>B.push(Number(te))),B.length!==0&&B.length!==Y&&h>=18&&B.length!==u.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(u.axes.length>0){if(w.length!==0&&w.length!==u.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(B.length!==0&&B.length!==u.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof w<"u"&&typeof B<"u"&&w.length>0&&B.length>Y)throw new Error("Resize requires only of scales or sizes to be specified")},vl=(n,u,h,w)=>`
  // The whole part and the fractional part are calculated separately due to inaccuracy of floating
  // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an
  // offset-by-one error later in floor().
  let big = (${n}) * (${u});
  let whole = ${w}(big / (${h}));
  let fract = ${w}(big % (${h})) / ${w}(${h});
  return whole + fract;
`,y1=(n,u)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${u} { `+(()=>{switch(n){case"asymmetric":return`
          if (xScale < 1.0 || floor(xScale) != xScale) {
            return ${u}(xResized) / ${u}(xScale);
          } else {
            ${vl("xResized","lengthOriginal","lengthResized",u)}
          }
        `;case"pytorch_half_pixel":return`if (lengthResized > 1) {
                    return (${u}(xResized) + 0.5) / ${u}(xScale) - 0.5;
                  } else {
                    return 0.0;
                  }`;case"tf_half_pixel_for_nn":return`return (${u}(xResized) + 0.5) / ${u}(xScale);`;case"align_corners":return`if (lengthResized == 1) {
                    return 0.0;
                  } else {
                    ${vl("xResized","lengthOriginal - 1","lengthResized - 1",u)}
                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {
                    return ${u}(roiStart) * ${u}(lengthOriginal - 1) +
                        (${u}(xResized) * ${u}(roiEnd - roiStart) * ${u}(lengthOriginal - 1)) /
                        ${u}(lengthResized - 1);
                  } else {
                    return 0.5 * ${u}(roiStart + roiEnd) * ${u}(lengthOriginal - 1);
                  }`;case"half_pixel_symmetric":return`const outputWidth = ${u}xScale * ${u}(lengthResized);
                  const adjustment = ${u}(lengthResized) / outputWidth;
                  const center = ${u}(lengthOriginal) / 2;
                  const offset = center * (1 - adjustment);
                  return offset + ((${u}(xResized) + 0.5) / ${u}(xScale)) - 0.5;`;case"half_pixel":return`return ((${u}(xResized) + 0.5) / ${u}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${n} is not supported`)}})()+"}",Qo=(n,u,h)=>`fn getNearestPixelFromOriginal(xOriginal: ${h}, isDownSample: bool) -> ${h} {`+(()=>{switch(n){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) {             return ceil(xOriginal);           } else {             return round(xOriginal);           }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) {                     return floor(xOriginal);                   } else {                     return round(xOriginal);                   }";default:if(u<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${n} is not supported`)}})()+"}",To=(n,u,h)=>{let w=new Array(h).fill(0).concat(new Array(h).fill(1)),B=n.length===0?w:n.slice();return u.length>0?(u.forEach((v,y)=>{w[v]=B[y],w[y+h]=B[u.length+y]}),w):B},yl=(n,u,h,w)=>{let B=[];if(h.length>0)if(w.length>0){if(n.forEach(v=>B.push(v)),Math.max(...w)>n.length)throw new Error("axes is out of bound");w.forEach((v,y)=>B[v]=h[y])}else h.forEach(v=>B.push(v));else{if(u.length===0)throw new Error("Resize requires either scales or sizes.");B=n.map((v,y)=>Math.round(v*u[y]))}return B},El=(n,u,h)=>{let w=(()=>{switch(h.keepAspectRatioPolicy){case"not_larger":return h.axes.length>0?Math.min(...h.axes.map(v=>u[v]),Number.MAX_VALUE):Math.min(...u,Number.MAX_VALUE);case"not_smaller":return h.axes.length>0?Math.max(...h.axes.map(v=>u[v]),Number.MIN_VALUE):Math.max(...u,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${h.keepAspectRatioPolicy} is not supported`)}})();u.fill(1,0,u.length);let B=n.slice();return h.axes.length>0?(h.axes.forEach(v=>u[v]=w),h.axes.forEach(v=>B[v]=Math.round(n[v]*u[v]))):(u.fill(w,0,u.length),B.forEach((v,y)=>B[y]=Math.round(v*u[y]))),B},Po=(n,u,h,w,B)=>`
    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${n.type.indices}) -> array<${n.type.value}, ${h.length}> {
      var original_indices: array<${n.type.value}, ${h.length}>;
      for (var i:u32 = 0; i < ${h.length}; i++) {
        var output_index = ${n.indicesGet("output_indices","i")};
        var scale = ${rt("uniforms.scales","i",w)};
        var roi_low = ${rt("uniforms.roi","i",B)};
        var roi_hi = ${rt("uniforms.roi",`i + ${u.length}`,B)};
        if (scale == 1.0) {
          original_indices[i] = ${n.type.value}(output_index);
        } else {
          var input_shape_i = ${rt("uniforms.input_shape","i",u.length)};
          var output_shape_i = ${rt("uniforms.output_shape","i",h.length)};
          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                           input_shape_i, roi_low, roi_hi);
        }
      }
      return original_indices;
    }`,Ml=(n,u,h,w,B,v,y)=>`
    fn calculateInputIndicesFromOutputIndices(output_indices: ${u.type.indices}) -> ${n.type.indices} {
      var input_indices: ${n.type.indices};
      for (var i:u32 = 0; i < ${w.length}; i++) {
        var output_index = ${u.indicesGet("output_indices","i")};
        var input_index: u32;
        var scale = ${rt("uniforms.scales","i",B)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${rt("uniforms.roi","i",v)};
          var roi_hi = ${rt("uniforms.roi",`i + ${h.length}`,v)};
          var input_shape_i = ${rt("uniforms.input_shape","i",h.length)};
          var output_shape_i = ${rt("uniforms.output_shape","i",w.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${y} || (original_idx >= 0 && original_idx < ${u.type.value}(input_shape_i))) {
            if (original_idx < 0) {
              input_index = 0;
            } else if (original_idx > ${u.type.value}(input_shape_i - 1)) {
              input_index = input_shape_i - 1;
            } else {
              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));
            }
          } else {
            input_index = u32(original_idx);
          }
        }
        ${n.indicesSet("input_indices","i","input_index")}
      }
      return input_indices;
    }`,Gl=(n,u)=>`
    fn checkInputIndices(input_indices: ${n.type.indices}) -> bool {
      for (var i:u32 = 0; i < ${u.length}; i++) {
        var input_index = ${n.indicesGet("input_indices","i")};
        if (input_index < 0 || input_index >= ${rt("uniforms.input_shape","i",u.length)}) {
          return false;
        }
      }
      return true;
    }`,_0=(n,u,h,w)=>n.rank>w?`
    ${n.indicesSet("input_indices",u,"channel")};
    ${n.indicesSet("input_indices",h,"batch")};
`:"",Dl=(n,u,h,w,B)=>{let[v,y,F,N]=h.length===2?[-1,0,1,-1]:[0,2,3,1],Y=n.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${Y} {
      var input_indices: ${n.type.indices};
      ${n.indicesSet("input_indices",y,`max(0, min(row, ${h[y]} - 1))`)};
      ${n.indicesSet("input_indices",F,`max(0, min(col, ${h[F]} - 1))`)};
      ${_0(n,N,v,2)}
      return ${n.getByIndices("input_indices")};
    }

    fn bilinearInterpolation(output_indices: ${u.type.indices}) -> ${Y} {
      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
      var row:${Y} = originalIndices[${y}];
      var col:${Y} = originalIndices[${F}];
      ${w?`if (row < 0 || row > (${h[y]} - 1) || col < 0 || col > (${h[F]} - 1)) {
        return ${B};
      }`:""};
      row = max(0, min(row, ${h[y]} - 1));
      col = max(0, min(col, ${h[F]} - 1));
      var row1: u32 = u32(row);
      var col1: u32 = u32(col);
      var row2: u32 = u32(row + 1);
      var col2: u32 = u32(col + 1);
      var channel: u32 = ${h.length>2?`u32(originalIndices[${N}])`:"0"};
      var batch: u32 =  ${h.length>2?`u32(originalIndices[${v}])`:"0"};
      var x11: ${Y} = getInputValue(batch, channel, row1, col1);
      var x12: ${Y} = getInputValue(batch, channel, row1, col2);
      var x21: ${Y} = getInputValue(batch, channel, row2, col1);
      var x22: ${Y} = getInputValue(batch, channel, row2, col2);
      var dx1: ${Y} = abs(row - ${Y}(row1));
      var dx2: ${Y} = abs(${Y}(row2) - row);
      var dy1: ${Y} = abs(col - ${Y}(col1));
      var dy2: ${Y} = abs(${Y}(col2) - col);
      if (row1 == row2) {
        dx1 = 0.5;
        dx2 = 0.5;
      }
      if (col1 == col2) {
        dy1 = 0.5;
        dy2 = 0.5;
      }
      return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);
    }`},Ql=(n,u,h,w,B,v,y,F,N,Y)=>{let te=h.length===2,[Q,ie]=te?[0,1]:[2,3],oe=n.type.value,de=fe=>{let ke=fe===Q?"row":"col";return`
      fn ${ke}CubicInterpolation(input_indices: ${n.type.indices}, output_indices: ${u.type.indices}) -> ${oe} {
        var output_index = ${u.indicesGet("output_indices",fe)};
        var originalIdx: ${oe} = getOriginalCoordinateFromResizedCoordinate(output_index, ${B[fe]},
        ${w[fe]}, ${h[fe]}, ${v[fe]}, ${v[fe]} + ${h.length});
        var fractOriginalIdx: ${oe} = originalIdx - floor(originalIdx);
        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);

        if (${F} && (originalIdx < 0 || originalIdx > (${h[fe]} - 1))) {
          return ${N};
        }
        var data: array<${oe}, 4> = array<${oe}, 4>(0.0, 0.0, 0.0, 0.0);
        for (var i: i32 = -1; i < 3; i++) {
          var ${ke}: ${oe} = originalIdx + ${oe}(i);
          if (${ke} < 0 || ${ke} >= ${h[fe]}) {
            ${Y?`coefs[i + 1] = 0.0;
                        continue;`:F?`return ${N};`:`${ke} = max(0, min(${ke}, ${h[fe]} - 1));`};
          }
        var input_indices_copy: ${n.type.indices} = input_indices;
          ${n.indicesSet("input_indices_copy",fe,`u32(${ke})`)};
          data[i + 1] = ${fe===Q?n.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};
        }
        return cubicInterpolation1D(data, coefs);
      }`};return`
    ${de(Q)};
    ${de(ie)};
  fn getCubicInterpolationCoefs(s: ${oe}) -> array<${oe}, 4> {
    var absS = abs(s);
    var coeffs: array<${oe}, 4> = array<${oe}, 4>(0.0, 0.0, 0.0, 0.0);
    var oneMinusAbsS: ${oe} = 1.0 - absS;
    var twoMinusAbsS: ${oe} = 2.0 - absS;
    var onePlusAbsS: ${oe} = 1.0 + absS;
    coeffs[0] = ((${y} * onePlusAbsS - 5 * ${y}) * onePlusAbsS + 8 * ${y}) * onePlusAbsS - 4 * ${y};
    coeffs[1] = ((${y} + 2) * absS - (${y} + 3)) * absS * absS + 1;
    coeffs[2] = ((${y} + 2) * oneMinusAbsS - (${y} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;
    coeffs[3] = ((${y} * twoMinusAbsS - 5 * ${y}) * twoMinusAbsS + 8 * ${y}) * twoMinusAbsS - 4 * ${y};
    return coeffs;
  }

  fn cubicInterpolation1D(x: array<${oe}, 4>, coefs: array<${oe}, 4>) -> ${oe} {
    var coefsSum: ${oe} = coefs[0] + coefs[1] + coefs[2] + coefs[3];
    return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;
  }

  fn bicubicInterpolation(output_indices: ${u.type.indices}) -> ${oe} {
    var input_indices: ${n.type.indices} = output_indices;
    return colCubicInterpolation(input_indices, output_indices);
  }
    `},zo=(n,u,h,w,B)=>{let[v,y,F,N,Y]=h.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],te=n.type.value;return`
    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${te} {
      var input_indices: ${n.type.indices};
      ${n.indicesSet("input_indices",y,`max(0, min(depth, ${h[y]} - 1))`)};
      ${n.indicesSet("input_indices",F,`max(0, min(height, ${h[F]} - 1))`)};
      ${n.indicesSet("input_indices",N,`max(0, min(width, ${h[N]} - 1))`)};
      ${_0(n,Y,v,3)}
      return ${n.getByIndices("input_indices")};
    }

    fn trilinearInterpolation(output_indices: ${u.type.indices}) -> ${te} {
      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
      var depth:${te} = originalIndices[${y}];
      var height:${te} = originalIndices[${F}];
      var width:${te} = originalIndices[${N}];
      ${w?`if (depth < 0 || depth > (${h[y]} - 1) || height < 0 || height > (${h[F]} - 1) || width < 0 || (width > ${h[N]} - 1)) {
      return ${B};
        }`:""};

    depth = max(0, min(depth, ${h[y]} - 1));
      height = max(0, min(height, ${h[F]} - 1));
      width = max(0, min(width, ${h[N]} - 1));
      var depth1: u32 = u32(depth);
      var height1: u32 = u32(height);
      var width1: u32 = u32(width);
      var depth2: u32 = u32(depth + 1);
      var height2: u32 = u32(height + 1);
      var width2: u32 = u32(width + 1);
      var channel: u32 = ${h.length>3?`u32(originalIndices[${Y}])`:"0"};
      var batch: u32 =  ${h.length>3?`u32(originalIndices[${v}])`:"0"};

      var x111: ${te} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${te} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${te} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${te} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${te} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${te} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${te} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${te} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${te} = abs(depth - ${te}(depth1));
      var dx2: ${te} = abs(${te}(depth2) - depth);
      var dy1: ${te} = abs(height - ${te}(height1));
      var dy2: ${te} = abs(${te}(height2) - height);
      var dz1: ${te} = abs(width - ${te}(width1));
      var dz2: ${te} = abs(${te}(width2) - width);
      if (depth1 == depth2) {
        dx1 = 0.5;
        dx2 = 0.5;
      }
      if (height1 == height2) {
        dy1 = 0.5;
        dy2 = 0.5;
      }
      if (width1 == width2) {
        dz1 = 0.5;
        dz2 = 0.5;
      }
      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +
              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);
    }`},Tl=(n,u,h,w,B,v)=>{let y=n.dims,F=To(v,u.axes,y.length),N=yl(y,w,B,u.axes),Y=w.slice();w.length===0&&(Y=y.map((Me,De)=>Me===0?1:N[De]/Me),u.keepAspectRatioPolicy!=="stretch"&&(N=El(y,Y,u)));let te=JA("output",n.dataType,N.length),Q=gA("input",n.dataType,y.length),ie=He.size(N),oe=y.length===N.length&&y.every((Me,De)=>Me===N[De]),de=u.coordinateTransformMode==="tf_crop_and_resize",fe=u.extrapolationValue,ke=Q.type.value;return{name:"Resize",shaderCache:{hint:`${u.cacheKey}|${h}|${Y.length>0?u.mode==="cubic"?Y:Y.length:""}|${B.length>0?B:""}|${F.length>0?F:""}|${oe}|${u.mode==="nearest"?y.length:y}`,inputDependencies:["rank"]},getShaderSource:Me=>`
      ${oe?"":`
      ${y1(u.coordinateTransformMode,ke)};
      ${(()=>{switch(u.mode){case"nearest":return`
              ${Gl(Q,y)};
              ${Qo(u.nearestMode,h,ke)};
              ${Ml(Q,te,y,N,Y.length,F.length,de)};
              `;case"linear":return`
              ${Po(te,y,N,Y.length,F.length)};
              ${(()=>{if(y.length===2||y.length===4)return`${Dl(Q,te,y,de,fe)}`;if(y.length===3||y.length===5)return`${zo(Q,te,y,de,fe)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(y.length===2||y.length===4)return`${Ql(Q,te,y,N,Y,F,u.cubicCoeffA,de,u.extrapolationValue,u.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};
            `;default:throw Error("Invalid resize mode")}})()};
      `}
      ${Me.registerUniform("output_size","u32").registerUniform("scales","f32",Y.length).registerUniform("roi","f32",F.length).declareVariables(Q,te)}
      ${Me.mainStart()}
        ${Me.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${oe?"output[global_idx] = input[global_idx];":`
        let output_indices = ${te.offsetToIndices("global_idx")};
        var input_indices: ${Q.type.indices};
        ${(()=>{switch(u.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${Q.getByIndices("input_indices")};
                } else {
                  output[global_idx] = ${u.extrapolationValue};
                }`;case"linear":return`output[global_idx] = ${y.length===2||y.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${u.mode}`)}})()};
`}
      }`,getRunData:()=>({outputs:[{dims:N,dataType:n.dataType}],dispatchGroup:{x:Math.ceil(ie/64)},programUniforms:[{type:12,data:ie},{type:1,data:Y},{type:1,data:F},...KA(y,N)]})}},Pl=n=>{let u=n.customDataBuffer;return new Uint32Array(u,u.byteOffset,1)[0]},zl=(n,u)=>{let h=[],w=[],B=[],v=Pl(n);if(u.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Do(n.inputs,u,v,h,w,B),n.compute(Tl(n.inputs[0],u,v,h,w,B),{inputs:[0]})},Fl=n=>{let u=n.antialias,h=n.axes,w=n.coordinateTransformMode,B=n.cubicCoeffA,v=n.excludeOutside!==0,y=n.extrapolationValue,F=n.keepAspectRatioPolicy,N=n.mode,Y=n.nearestMode===""?"simple":n.nearestMode;return $e({antialias:u,axes:h,coordinateTransformMode:w,cubicCoeffA:B,excludeOutside:v,extrapolationValue:y,keepAspectRatioPolicy:F,mode:N,nearestMode:Y})}}),Mr=Pe(()=>{Dt(),St(),qr(),Rt(),Sl=(n,u)=>{let[h,w,B,v]=n,{numHeads:y,rotaryEmbeddingDim:F}=u;if(h.dims.length!==3&&h.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${h.dims.length}`);if(!He.areEqual(w.dims,[])&&!He.areEqual(w.dims,[1])&&w.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${w.dims.length}`);if(B.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${B.dims.length}`);if(v.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${v.dims.length}`);if(!He.areEqual(B.dims,v.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(F>0&&y===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let N=h.dims[0],Y=h.dims[h.dims.length-2],te=B.dims[0],Q=He.sizeFromDimension(h.dims,1)/Y,ie=F===0?2*B.dims[1]:Q/y;if(F>ie)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(w.dims.length===2){if(N!==w.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${w.dims[0]}`);if(Y!==w.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${w.dims[1]}`)}if(ie/2!==B.dims[1]&&F/2!==B.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${B.dims[1]}`);if(Y>te)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Nl=(n,u)=>{let{interleaved:h,numHeads:w,rotaryEmbeddingDim:B,scale:v}=u,y=n[0].dims[0],F=He.sizeFromDimension(n[0].dims,1),N=n[0].dims[n[0].dims.length-2],Y=F/N,te=n[2].dims[1],Q=B===0?2*te:Y/w,ie=new Array(y,N,Y/Q,Q-te),oe=He.computeStrides(ie),de=[{type:1,data:v},{type:12,data:ie},{type:12,data:oe},...n[0].dims.length===3?new Array({type:12,data:[F,Y,Q,1]}):[],...n[0].dims.length===4?new Array({type:12,data:[F,Q,N*Q,1]}):[],...KA(n[0].dims,n[1].dims,n[2].dims,n[3].dims,n[0].dims)];return{name:"RotaryEmbedding",shaderCache:{hint:$e({interleaved:h}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:fe=>{let ke=gA("input",n[0].dataType,n[0].dims.length),Me=gA("position_ids",n[1].dataType,n[1].dims.length),De=gA("cos_cache",n[2].dataType,n[2].dims.length),Le=gA("sin_cache",n[3].dataType,n[3].dims.length),Xe=JA("output",n[0].dataType,n[0].dims.length);return fe.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:ie.length},{name:"global_strides",type:"u32",length:oe.length},{name:"input_output_strides",type:"u32",length:oe.length}]),`
        ${fe.declareVariables(ke,Me,De,Le,Xe)}

        ${fe.mainStart(cr)}
          let half_rotary_emb_dim = uniforms.${De.name}_shape[1];
          let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape;
          let size = uniforms.global_shape[0] * uniforms.global_strides[0];
          ${fe.guardAgainstOutOfBoundsWorkgroupSizes("size")}

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${Me.broadcastedIndicesToOffset("bsnh.xy",JA("",Me.type.tensor,2))};
            let position_id =
                u32(${Me.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);
            let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${h});
            let j = i + select(half_rotary_emb_dim, 1, ${h});
            let re = ${ke.getByOffset("i")} * ${De.get("position_id","bsnh[3]")} -
                ${ke.getByOffset("j")} * ${Le.get("position_id","bsnh[3]")};
            ${Xe.setByOffset("i","re")}
            let im = ${ke.getByOffset("i")} * ${Le.get("position_id","bsnh[3]")} +
                ${ke.getByOffset("j")} * ${De.get("position_id","bsnh[3]")};
            ${Xe.setByOffset("j","im")}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${Xe.setByOffset("k",ke.getByOffset("k"))}
          }
        }`},getRunData:()=>({outputs:[{dims:n[0].dims,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(He.size(ie)/cr)},programUniforms:de})}},Ol=(n,u)=>{Sl(n.inputs,u),n.compute(Nl(n.inputs,u))}}),Vr=Pe(()=>{Dt(),St(),Rt(),Ll=n=>{if(!n||n.length<3)throw new Error("layerNorm requires at least 3 inputs.");let u=n[0],h=n[1],w=n[2];if(u.dataType!==h.dataType||u.dataType!==w.dataType)throw new Error("All inputs must have the same data type");if(u.dims.length!==3&&u.dims.length!==2)throw new Error("Input must be 2D or 3D");if(h.dims.length!==3&&h.dims.length!==2)throw new Error("Skip must be 2D or 3D");let B=u.dims[u.dims.length-1],v=u.dims[u.dims.length-2];if(h.dims[h.dims.length-1]!==B)throw new Error("Skip must have the same hidden size as input");if(h.dims[h.dims.length-2]!==v)throw new Error("Skip must have the same sequence length as input");if(w.dims.length!==1)throw new Error("Gamma must be 1D");if(w.dims[w.dims.length-1]!==B)throw new Error("Gamma must have the same hidden size as input");if(n.length>3){let y=n[3];if(y.dims.length!==1)throw new Error("Beta must be 1D");if(y.dims[y.dims.length-1]!==B)throw new Error("Beta must have the same hidden size as input")}if(n.length>4){let y=n[4];if(y.dims.length!==1)throw new Error("Bias must be 1D");if(y.dims[y.dims.length-1]!==B)throw new Error("Bias must have the same hidden size as input")}},Yl=(n,u,h,w)=>{let B=u.simplified,v=n[0].dims,y=He.size(v),F=v,N=y,Y=v.slice(-1)[0],te=w?v.slice(0,-1).concat(1):[],Q=!B&&n.length>3,ie=n.length>4,oe=w&&h>1,de=w&&h>2,fe=h>3,ke=64,Me=Jt(Y),De=[{type:12,data:N},{type:12,data:Me},{type:12,data:Y},{type:1,data:u.epsilon}],Le=[{dims:F,dataType:n[0].dataType}];return h>1&&Le.push({dims:te,dataType:1}),h>2&&Le.push({dims:te,dataType:1}),h>3&&Le.push({dims:v,dataType:n[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${Me};${oe};${de};${fe}`,inputDependencies:n.map((Xe,_e)=>"type")},getShaderSource:Xe=>{let _e=[gA("x",n[0].dataType,n[0].dims,Me),gA("skip",n[1].dataType,n[1].dims,Me),gA("gamma",n[2].dataType,n[2].dims,Me)];Q&&_e.push(gA("beta",n[3].dataType,n[3].dims,Me)),ie&&_e.push(gA("bias",n[4].dataType,n[4].dims,Me)),_e.push(JA("output",n[0].dataType,F,Me)),oe&&_e.push(JA("mean_output",1,te)),de&&_e.push(JA("inv_std_output",1,te)),fe&&_e.push(JA("input_skip_bias_sum",n[0].dataType,F,Me));let lA=jt(n[0].dataType),LA=jt(1,Me);return`

      ${Xe.registerUniforms([{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}]).declareVariables(..._e)}
      var<workgroup> sum_shared : array<${LA}, 64>;
      var<workgroup> sum_squared_shared : array<${LA}, 64>;

      ${Xe.mainStart([ke,1,1])}
        let ix = local_id.x;
        let iy = global_id.x / 64;

        let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;
        var stride = hidden_size_vectorized / 64;
        let offset = ix * stride + iy * hidden_size_vectorized;
        let offset1d = stride * ix;
        if (ix == 63) {
          stride = hidden_size_vectorized - stride * ix;
        }
        for (var i: u32 = 0; i < stride; i++) {
          let skip_value = skip[offset + i];
          let bias_value = ${ie?"bias[offset1d + i]":lA+"(0.0)"};
          let input_value = x[offset + i];
          let value = input_value + skip_value + bias_value;
          ${fe?"input_skip_bias_sum[offset + i] = value;":""}
          output[offset + i] = value;
          let f32_value = ${fa(lA,Me,"value")};
          sum_shared[ix] += f32_value;
          sum_squared_shared[ix] += f32_value * f32_value;
        }
        workgroupBarrier();

        var reduce_size : u32 = 64;
        for (var curr_size = reduce_size >> 1;  curr_size > 0; curr_size = reduce_size >> 1) {
          reduce_size = curr_size + (reduce_size & 1);
          if (ix < curr_size) {
            sum_shared[ix] += sum_shared[ix + reduce_size];
            sum_squared_shared[ix] += sum_squared_shared[ix + reduce_size];
          }
          workgroupBarrier();
        }

        let sum = sum_shared[0];
        let square_sum = sum_squared_shared[0];
        let mean = ${ir("sum",Me)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${ir("square_sum",Me)} / f32(uniforms.hidden_size) ${B?"":"- mean * mean"} + uniforms.epsilon);
        ${oe?"mean_output[global_idx] = mean;":""}
        ${de?"inv_std_output[global_idx] = inv_std_dev;":""}

        for (var i: u32 = 0; i < stride; i++) {
          output[offset + i] = (output[offset + i] ${B?"":`- ${lA}(mean)`}) *
            ${lA}(inv_std_dev) * gamma[offset1d + i]
            ${Q?"+ beta[offset1d + i]":""};
        }
      }`},getRunData:()=>({outputs:Le,dispatchGroup:{x:Math.ceil(N/Y)},programUniforms:De})}},Fo=(n,u)=>{Ll(n.inputs);let h=[0];n.outputCount>1&&h.push(-3),n.outputCount>2&&h.push(-3),n.outputCount>3&&h.push(3),n.compute(Yl(n.inputs,u,n.outputCount,!1),{outputs:h})}}),Jr=Pe(()=>{Dt(),St(),qr(),Rt(),Hl=(n,u)=>{if(!n||n.length<1)throw new Error("too few inputs");if(u.axes.length!==0){if(u.axes.length!==u.starts.length||u.axes.length!==u.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(u.starts.length!==u.ends.length)throw new Error("starts and ends must have the same length");n.slice(1).forEach((h,w)=>{if(n[w+1].dataType!==6&&n[w+1].dataType!==7)throw new Error(`Input ${w} must be an array of int32 or int64`)})},S0=(n,u)=>{let h=[];if(n.length>u)if(n[u].dataType===7)n[u].getBigInt64Array().forEach(w=>h.push(Number(w)));else{if(n[u].dataType!==6)throw new Error(`Input ${u} must be an array of int32 or int64`);n[u].getInt32Array().forEach(w=>h.push(Number(w)))}return h},So=(n,u)=>{if(n.length>1){let h=S0(n,1),w=S0(n,2),B=S0(n,3);return B.length===0&&(B=[...Array(n[0].dims.length).keys()]),$e({starts:h,ends:w,axes:B})}return u},No=(n,u,h,w,B)=>{let v=n;return n<0&&(v+=h[w[u]]),B[u]<0?Math.max(0,Math.min(v,h[w[u]]-1)):Math.max(0,Math.min(v,h[w[u]]))},Rl=(n,u,h)=>`fn calculateInputIndices(output_indices: ${u.type.indices}) -> ${n.type.indices} {
          var input_indices: ${n.type.indices};
          var carry = 0u;
          for (var i = ${h.length}; i >= 0; i--) {
            let input_shape_i = ${rt("uniforms.input_shape","i",h.length)};
            let steps_i = ${rt("uniforms.steps","i",h.length)};
            let signs_i = ${rt("uniforms.signs","i",h.length)};
            let starts_i = ${rt("uniforms.starts","i",h.length)};
            var output_index = ${u.indicesGet("output_indices","i")};
            var input_index = output_index * steps_i + starts_i + carry;
            carry = input_index / input_shape_i;
            input_index = input_index % input_shape_i;
            if (signs_i < 0) {
              input_index = input_shape_i - input_index - 1u + starts_i;
            }
            ${n.indicesSet("input_indices","i","input_index")};
          }
          return input_indices;
      }`,br=(n,u)=>{let h=n[0].dims,w=He.size(h),B=u.axes.length>0?He.normalizeAxes(u.axes,h.length):[...Array(h.length).keys()],v=S0(n,4);v.forEach(ke=>ke!==0||(()=>{throw new Error("step cannot be 0")})),v.length===0&&(v=Array(B.length).fill(1));let y=u.starts.map((ke,Me)=>No(ke,Me,h,B,v)),F=u.ends.map((ke,Me)=>No(ke,Me,h,B,v));if(B.length!==y.length||B.length!==F.length)throw new Error("start, ends and axes should have the same number of elements");if(B.length!==h.length)for(let ke=0;ke<h.length;++ke)B.includes(ke)||(y.splice(ke,0,0),F.splice(ke,0,h[ke]),v.splice(ke,0,1));let N=v.map(ke=>Math.sign(ke));v.forEach((ke,Me,De)=>{if(ke<0){let Le=(F[Me]-y[Me])/ke,Xe=y[Me],_e=Xe+Le*v[Me];y[Me]=_e,F[Me]=Xe,De[Me]=-ke}});let Y=h.slice(0);B.forEach((ke,Me)=>{Y[ke]=Math.ceil((F[ke]-y[ke])/v[ke])});let te={dims:Y,dataType:n[0].dataType},Q=JA("output",n[0].dataType,Y.length),ie=gA("input",n[0].dataType,n[0].dims.length),oe=He.size(Y),de=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:y.length},{name:"signs",type:"i32",length:N.length},{name:"steps",type:"u32",length:v.length}],fe=[{type:12,data:oe},{type:12,data:y},{type:6,data:N},{type:12,data:v},...KA(n[0].dims,Y)];return{name:"Slice",shaderCache:{hint:`${N.length}_${y.length}_${v.length}`,inputDependencies:["rank"]},getShaderSource:ke=>`
      ${ke.registerUniforms(de).declareVariables(ie,Q)}
        ${Rl(ie,Q,h)}
        ${ke.mainStart()}
          ${ke.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
          let output_indices = ${Q.offsetToIndices("global_idx")};
          let input_indices = calculateInputIndices(output_indices);
          ${Q.setByOffset("global_idx",ie.getByIndices("input_indices"))}
      }`,getRunData:()=>({outputs:[te],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:fe})}},E1=(n,u)=>{Hl(n.inputs,u);let h=So(n.inputs,u);n.compute(br(n.inputs,h),{inputs:[0]})},M1=n=>{let u=n.starts,h=n.ends,w=n.axes;return $e({starts:u,ends:h,axes:w})}}),Sa=Pe(()=>{Dt(),St(),qr(),tn(),Rt(),G1=n=>{if(!n||n.length!==1)throw new Error("Softmax op requires 1 input.")},Oo=(n,u)=>{let h,w=n.inputs[0],B=w.dims,v=He.size(B),y=B.length,F=He.normalizeAxis(u.axis,y),N=F<B.length-1,Y=[];N?(Y=Array.from({length:y},(_e,lA)=>lA),Y[F]=y-1,Y[y-1]=F,h=n.compute(Ma(w,Y),{inputs:[w],outputs:[-1]})[0]):h=w;let te=h.dims,Q=te[y-1],ie=v/Q,oe=Jt(Q),de=Q/oe,fe=64;ie===1&&(fe=256);let ke=gA("x",h.dataType,h.dims,oe),Me=JA("result",h.dataType,h.dims,oe),De=ke.type.value,Le=jt(h.dataType)==="f32"?`var threadMax = ${De}(-3.402823e+38f);`:`var threadMax = ${De}(-65504.0h);`,Xe=n.compute({name:"Softmax",shaderCache:{hint:`${oe};${fe}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:te,dataType:h.dataType}],dispatchGroup:{x:ie},programUniforms:[{type:6,data:de}]}),getShaderSource:_e=>`
      var<workgroup> rowMaxShared : ${De};
      var<workgroup> rowSumShared : ${De};
      var<workgroup> threadShared : array<${De}, ${fe}>;

      fn getValue(row: i32, col: i32, row_stride: i32) -> ${De} {
        let index = row * row_stride + col;
        return x[index];
      }

      fn setValue(row: i32, col: i32, row_stride: i32, value: ${De}) {
        let index = row * row_stride + col;
        result[index] = value;
      }
      ${_e.registerUniform("packedCols","i32").declareVariables(ke,Me)}
      ${_e.mainStart(fe)}
        let gindex = i32(global_idx);
        let lindex = i32(local_idx);
        const wg = ${fe};
        let row = gindex / wg;
        let cols = uniforms.packedCols;
        let row_stride : i32 = uniforms.packedCols;

        // find the rows max
        ${Le}
        for (var col = lindex; col < cols; col += wg) {
          let value = getValue(row, col, row_stride);
          threadMax = max(threadMax, value);
        }
        if (lindex < cols) {
          threadShared[lindex] = threadMax;
        }
        workgroupBarrier();

        var reduceSize = min(cols, wg);
        for (var currSize = reduceSize >> 1;  currSize > 0; currSize = reduceSize >> 1) {
          reduceSize = currSize + (reduceSize & 1);
          if (lindex < currSize) {
            threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]);
          }
          workgroupBarrier();
        }
        if (lindex == 0) {
          rowMaxShared = ${De}(${((lA,LA)=>LA===4?`max(max(${lA}.x, ${lA}.y), max(${lA}.z, ${lA}.w))`:LA===2?`max(${lA}.x, ${lA}.y)`:LA===3?`max(max(${lA}.x, ${lA}.y), ${lA}.z)`:lA)("threadShared[0]",oe)});
        }
        workgroupBarrier();

        // find the rows sum
        var threadSum = ${De}(0.0);
        for (var col = lindex; col < cols; col += wg) {
          let subExp = exp(getValue(row, col, row_stride) - rowMaxShared);
          threadSum += subExp;
        }
        threadShared[lindex] = threadSum;
        workgroupBarrier();

        for (var currSize = wg >> 1;  currSize > 0; currSize = currSize >> 1) {
          if (lindex < currSize) {
            threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];
          }
          workgroupBarrier();
        }
        if (lindex == 0) {
          rowSumShared = ${De}(${ir("threadShared[0]",oe)});
        }
        workgroupBarrier();

        // calculate final value for each element in the row
        for (var col = lindex; col < cols; col += wg) {
          let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;
          setValue(row, col, row_stride, value);
        }
      }`},{inputs:[h],outputs:[N?-1:0]})[0];N&&n.compute(Ma(Xe,Y),{inputs:[Xe]})},Wl=(n,u)=>{G1(n.inputs),Oo(n,u)},Vl=n=>$e({axis:n.axis})}),_r=Pe(()=>{Dt(),St(),Rt(),Lo=n=>Array.from(n.getBigInt64Array(),Number),jl=n=>{if(!n||n.length!==2)throw new Error("Tile requires 2 inputs.");if(n[0].dataType!==1&&n[0].dataType!==10&&n[0].dataType!==6&&n[0].dataType!==12)throw new Error("Tile only support float, float16, int32, and uint32 data types");if(n[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(n[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(Lo(n[1]).length!==n[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},Yo=(n,u)=>{let h=[];for(let w=0;w<n.length;++w)h.push(n[w]*u[w]);return h},Ho=(n,u)=>{let h=n[0].dims,w=u??Lo(n[1]),B=Yo(h,w),v=He.size(B),y=n[0].dataType,F=gA("input",y,h.length),N=JA("output",y,B.length);return{name:"Tile",shaderCache:{hint:`${w}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:B,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(v/64)},programUniforms:[{type:12,data:v},...KA(n[0].dims,B)]}),getShaderSource:Y=>`
      const inputShape = ${F.indices(...h)};
      ${Y.registerUniform("output_size","u32").declareVariables(F,N)}
      ${Y.mainStart()}
      ${Y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let output_indices = ${N.offsetToIndices("global_idx")};
      var input_indices: ${F.type.indices};
      for (var i = 0; i < ${h.length}; i++) {
        let input_dim_i = ${F.indicesGet("uniforms.input_shape","i")};
        let input_dim_value = ${N.indicesGet("output_indices","i")}  % input_dim_i;

        ${F.indicesSet("input_indices","i","input_dim_value")}
      }
      ${N.setByOffset("global_idx",F.getByIndices("input_indices"))}
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this.mlContext===n&&this.dataType===u&&this.tensorShape.length===h.length&&this.tensorShape.every((w,B)=>w===h[B])}},Uo=class{constructor(n,u){this.tensorManager=n,this.wrapper=u}get tensorWrapper(){return this.wrapper}releaseTensor(){this.tensorWrapper&&(this.tensorManager.releaseTensor(this.tensorWrapper),this.wrapper=void 0)}async ensureTensor(n,u,h,w){if(this.wrapper){if(this.wrapper.canReuseTensor(n,u,h))return this.wrapper.tensor;if(w){if(this.wrapper.byteLength!==Vo(u,h))throw new Error("Unable to copy data to tensor with different size.");this.activeUpload=new Uint8Array(await this.wrapper.read())}this.tensorManager.releaseTensor(this.wrapper)}let B=typeof MLTensorUsage>"u"?void 0:MLTensorUsage.READ|MLTensorUsage.WRITE;return this.wrapper=await this.tensorManager.getCachedTensor(u,h,B,!0,!0),w&&this.activeUpload&&(this.wrapper.write(this.activeUpload),this.activeUpload=void 0),this.wrapper.tensor}upload(n){if(this.wrapper){if(n.byteLength===this.wrapper.byteLength)return void this.wrapper.write(n);Pt("verbose",()=>"Data size does not match tensor size. 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u=He.size(this.dims);return u===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,u)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let u=He.size(this.dims);return u===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,u)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let u=He.size(this.dims);return u===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,u)}getUint16Array(){if(this.dataType!==10&&this.dataType!==4)throw new Error("Invalid data type");let u=He.size(this.dims);return u===0?new Uint16Array:new Uint16Array(this.module.HEAP8.buffer,this.data,u)}reshape(u){if(He.size(u)!==He.size(this.dims))throw new Error("Invalid new shape");return new $u(this.module,this.dataType,this.data,u)}},u1=class{constructor(n,u,h){this.module=n,this.backend=u,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=u.adapterInfo,this.deviceInfo=u.deviceInfo;let w=n.PTR_SIZE,B=h/n.PTR_SIZE,v=w===4?"i32":"i64";this.opKernelContext=Number(n.getValue(w*B++,v));let y=Number(n.getValue(w*B++,v));this.outputCount=Number(n.getValue(w*B++,v)),this.customDataOffset=Number(n.getValue(w*B++,"*")),this.customDataSize=Number(n.getValue(w*B++,v));let F=[];for(let N=0;N<y;N++){let Y=Number(n.getValue(w*B++,v)),te=Number(n.getValue(w*B++,"*")),Q=Number(n.getValue(w*B++,v)),ie=[];for(let oe=0;oe<Q;oe++)ie.push(Number(n.getValue(w*B++,v)));F.push(new qn(n,Y,te,ie))}this.inputs=F}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}compute(n,u){let h=u?.inputs?.map(B=>typeof B=="number"?this.inputs[B]:B)??this.inputs,w=u?.outputs??[];return this.backend.run(n,h,w,(B,v,y)=>new qn(this.module,v,this.output(B,y),y),(B,v)=>{let y=ds(B,v);if(!y)throw new Error(`Unsupported data type: ${B}`);let 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If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${w}`)}finally{this.module.stackRestore(h)}}},d1=async(n,u,h,w)=>{let B=u.jsepInit;if(!B)throw new Error("Failed to initialize JSEP. 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k.Callable{constructor(b){super(),this.image_mean=b.image_mean??b.mean,this.image_std=b.image_std??b.std,this.resample=b.resample??2,this.do_rescale=b.do_rescale??!0,this.rescale_factor=b.rescale_factor??.00392156862745098,this.do_normalize=b.do_normalize,this.do_thumbnail=b.do_thumbnail,this.size=b.size??b.image_size,this.do_resize=b.do_resize??this.size!==void 0,this.size_divisibility=b.size_divisibility??b.size_divisor,this.do_center_crop=b.do_center_crop,this.crop_size=b.crop_size,this.do_convert_rgb=b.do_convert_rgb??!0,this.do_crop_margin=b.do_crop_margin,this.pad_size=b.pad_size,this.do_pad=b.do_pad,this.do_pad&&!this.pad_size&&this.size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size),this.do_flip_channel_order=b.do_flip_channel_order??!1,this.config=b}async thumbnail(b,e,l=2){const M=b.height,ue=b.width,D=e.height,z=e.width;let Re=Math.min(M,D),Ne=Math.min(ue,z);return Re===M&&Ne===ue?b:(M>ue?Ne=Math.floor(ue*Re/M):ue>M&&(Re=Math.floor(M*Ne/ue)),await b.resize(Ne,Re,{resample:l}))}async crop_margin(b,e=200){const l=b.clone().grayscale(),M=(0,j.min)(l.data)[0],ue=(0,j.max)(l.data)[0]-M;if(ue===0)return b;const D=e/255;let z=l.width,Re=l.height,Ne=0,Ye=0;const Je=l.data;for(let AA=0;AA<l.height;++AA){const xA=AA*l.width;for(let GA=0;GA<l.width;++GA)(Je[xA+GA]-M)/ue<D&&(z=Math.min(z,GA),Re=Math.min(Re,AA),Ne=Math.max(Ne,GA),Ye=Math.max(Ye,AA))}return b=await b.crop([z,Re,Ne,Ye])}pad_image(b,e,l,{mode:M="constant",center:ue=!1,constant_values:D=0}={}){const[z,Re,Ne]=e;let Ye,Je;if(typeof l=="number"?(Ye=l,Je=l):l==="square"?Ye=Je=Math.max(z,Re):(Ye=l.width,Je=l.height),Ye!==Re||Je!==z){const AA=new Float32Array(Ye*Je*Ne);if(Array.isArray(D))for(let Ge=0;Ge<AA.length;++Ge)AA[Ge]=D[Ge%Ne];else D!==0&&AA.fill(D);const[xA,GA]=ue?[Math.floor((Ye-Re)/2),Math.floor((Je-z)/2)]:[0,0];for(let Ge=0;Ge<z;++Ge){const Ie=(Ge+GA)*Ye,ze=Ge*Re;for(let Ue=0;Ue<Re;++Ue){const Pe=(Ie+Ue+xA)*Ne,kA=(ze+Ue)*Ne;for(let aA=0;aA<Ne;++aA)AA[Pe+aA]=b[kA+aA]}}if(M==="symmetric"){if(ue)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const Ge=z-1,Ie=Re-1;for(let ze=0;ze<Je;++ze){const Ue=ze*Ye,Pe=(0,O.calculateReflectOffset)(ze,Ge)*Re;for(let kA=0;kA<Ye;++kA){if(ze<z&&kA<Re)continue;const aA=(Ue+kA)*Ne,iA=(Pe+(0,O.calculateReflectOffset)(kA,Ie))*Ne;for(let yA=0;yA<Ne;++yA)AA[aA+yA]=b[iA+yA]}}}b=AA,e=[Je,Ye,Ne]}return[b,e]}rescale(b){for(let e=0;e<b.length;++e)b[e]=this.rescale_factor*b[e]}get_resize_output_image_size(b,e){const[l,M]=b.size;let ue,D;if(this.do_thumbnail){const{height:z,width:Re}=e;ue=Math.min(z,Re)}else Number.isInteger(e)?(ue=e,D=this.config.max_size??ue):e!==void 0&&(ue=e.shortest_edge,D=e.longest_edge);if(ue!==void 0||D!==void 0){const z=ue===void 0?1:Math.max(ue/l,ue/M),Re=l*z,Ne=M*z,Ye=D===void 0?1:Math.min(D/Re,D/Ne);let Je=Math.floor(Number((Re*Ye).toFixed(2))),AA=Math.floor(Number((Ne*Ye).toFixed(2)));return this.size_divisibility!==void 0&&([Je,AA]=S([Je,AA],this.size_divisibility)),[Je,AA]}if(e!==void 0&&e.width!==void 0&&e.height!==void 0){let z=e.width,Re=e.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let Ne=Re/M,Ye=z/l;Math.abs(1-Ye)<Math.abs(1-Ne)?Ne=Ye:Ye=Ne,Re=R(Ne*M,this.config.ensure_multiple_of),z=R(Ye*l,this.config.ensure_multiple_of)}return[z,Re]}if(this.size_divisibility!==void 0)return S([l,M],this.size_divisibility);if(e.min_pixels!==void 0&&e.max_pixels!==void 0){const{min_pixels:z,max_pixels:Re}=e;return function(Ne,Ye,Je=28,AA=3136,xA=1003520){if(Ne<Je||Ye<Je)throw new Error(`height:${Ne} or width:${Ye} must be larger than factor:${Je}`);if(Math.max(Ne,Ye)/Math.min(Ne,Ye)>200)throw new Error("absolute aspect ratio must be smaller than 200, got "+Math.max(Ne,Ye)/Math.min(Ne,Ye));let GA=Math.round(Ne/Je)*Je,Ge=Math.round(Ye/Je)*Je;if(GA*Ge>xA){const Ie=Math.sqrt(Ne*Ye/xA);GA=Math.floor(Ne/Ie/Je)*Je,Ge=Math.floor(Ye/Ie/Je)*Je}else if(GA*Ge<AA){const Ie=Math.sqrt(AA/(Ne*Ye));GA=Math.ceil(Ne*Ie/Je)*Je,Ge=Math.ceil(Ye*Ie/Je)*Je}return[GA,Ge]}(M,l,this.config.patch_size*this.config.merge_size,z,Re)}throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(e)}`)}async resize(b){const[e,l]=this.get_resize_output_image_size(b,this.size);return await b.resize(e,l,{resample:this.resample})}async preprocess(b,{do_normalize:e=null,do_pad:l=null,do_convert_rgb:M=null,do_convert_grayscale:ue=null,do_flip_channel_order:D=null}={}){this.do_crop_margin&&(b=await this.crop_margin(b));const[z,Re]=b.size;if(M??this.do_convert_rgb?b=b.rgb():ue&&(b=b.grayscale()),this.do_resize&&(b=await this.resize(b)),this.do_thumbnail&&(b=await this.thumbnail(b,this.size,this.resample)),this.do_center_crop){let AA,xA;Number.isInteger(this.crop_size)?(AA=this.crop_size,xA=this.crop_size):(AA=this.crop_size.width,xA=this.crop_size.height),b=await b.center_crop(AA,xA)}const Ne=[b.height,b.width];let Ye=Float32Array.from(b.data),Je=[b.height,b.width,b.channels];if(this.do_rescale&&this.rescale(Ye),e??this.do_normalize){let AA=this.image_mean;Array.isArray(this.image_mean)||(AA=new Array(b.channels).fill(AA));let xA=this.image_std;if(Array.isArray(this.image_std)||(xA=new Array(b.channels).fill(AA)),AA.length!==b.channels||xA.length!==b.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${AA.length}) and \`image_std\` (${xA.length}) must match the number of channels in the image (${b.channels}).`);for(let GA=0;GA<Ye.length;GA+=b.channels)for(let Ge=0;Ge<b.channels;++Ge)Ye[GA+Ge]=(Ye[GA+Ge]-AA[Ge])/xA[Ge]}if(l??this.do_pad){if(this.pad_size)[Ye,Je]=this.pad_image(Ye,[b.height,b.width,b.channels],this.pad_size);else if(this.size_divisibility){const[AA,xA]=S([Je[1],Je[0]],this.size_divisibility);[Ye,Je]=this.pad_image(Ye,Je,{width:AA,height:xA})}}if(D??this.do_flip_channel_order){if(Je[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let AA=0;AA<Ye.length;AA+=3){const xA=Ye[AA];Ye[AA]=Ye[AA+2],Ye[AA+2]=xA}}return{original_size:[Re,z],reshaped_input_size:Ne,pixel_values:new P.Tensor("float32",Ye,Je).permute(2,0,1)}}async _call(b,...e){Array.isArray(b)||(b=[b]);const l=await Promise.all(b.map(ue=>this.preprocess(ue)));return{pixel_values:(0,P.stack)(l.map(ue=>ue.pixel_values),0),original_sizes:l.map(ue=>ue.original_size),reshaped_input_sizes:l.map(ue=>ue.reshaped_input_size)}}static async from_pretrained(b,e){return new this(await(0,V.getModelJSON)(b,q.IMAGE_PROCESSOR_NAME,!0,e))}}},"./src/base/processing_utils.js":(x,C,d)=>{d.r(C),d.d(C,{Processor:()=>O});var k=d("./src/utils/constants.js"),P=d("./src/utils/generic.js"),j=d("./src/utils/hub.js");class O extends P.Callable{static classes=["image_processor_class","tokenizer_class","feature_extractor_class"];static uses_processor_config=!1;constructor(q,R){super(),this.config=q,this.components=R}get image_processor(){return this.components.image_processor}get tokenizer(){return this.components.tokenizer}get feature_extractor(){return this.components.feature_extractor}apply_chat_template(q,R={}){if(!this.tokenizer)throw new Error("Unable to apply chat template without a tokenizer.");return this.tokenizer.apply_chat_template(q,{tokenize:!1,...R})}batch_decode(...q){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.batch_decode(...q)}decode(...q){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.decode(...q)}async _call(q,...R){for(const S of[this.image_processor,this.feature_extractor,this.tokenizer])if(S)return S(q,...R);throw new Error("No image processor, feature extractor, or tokenizer found.")}static async from_pretrained(q,R){const[S,X]=await Promise.all([this.uses_processor_config?(0,j.getModelJSON)(q,k.PROCESSOR_NAME,!0,R):{},Promise.all(this.classes.filter(H=>H in this).map(async H=>{const re=await this[H].from_pretrained(q,R);return[H.replace(/_class$/,""),re]})).then(Object.fromEntries)]);return new this(S,X)}}},"./src/configs.js":(x,C,d)=>{d.r(C),d.d(C,{AutoConfig:()=>q,PretrainedConfig:()=>V,getKeyValueShapes:()=>O});var k=d("./src/utils/core.js"),P=d("./src/utils/hub.js");function j(R){const S={};let X={};switch(R.model_type){case"llava":case"paligemma":case"florence2":case"llava_onevision":case"idefics3":X=j(R.text_config);break;case"moondream1":X=j(R.phi_config);break;case"musicgen":X=j(R.decoder);break;case"multi_modality":X=j(R.language_config);break;case"gpt2":case"gptj":case"jais":case"codegen":case"gpt_bigcode":S.num_heads="n_head",S.num_layers="n_layer",S.hidden_size="n_embd";break;case"gpt_neox":case"stablelm":case"opt":case"falcon":S.num_heads="num_attention_heads",S.num_layers="num_hidden_layers",S.hidden_size="hidden_size";break;case"llama":case"olmo":case"olmo2":case"mobilellm":case"granite":case"cohere":case"mistral":case"starcoder2":case"qwen2":case"qwen2_vl":case"phi":case"phi3":case"phi3_v":S.num_heads="num_key_value_heads",S.num_layers="num_hidden_layers",S.hidden_size="hidden_size",S.num_attention_heads="num_attention_heads";break;case"gemma":case"gemma2":case"glm":case"helium":S.num_heads="num_key_value_heads",S.num_layers="num_hidden_layers",S.dim_kv="head_dim";break;case"openelm":S.num_heads="num_kv_heads",S.num_layers="num_transformer_layers",S.dim_kv="head_dim";break;case"gpt_neo":case"donut-swin":S.num_heads="num_heads",S.num_layers="num_layers",S.hidden_size="hidden_size";break;case"bloom":S.num_heads="n_head",S.num_layers="n_layer",S.hidden_size="hidden_size";break;case"mpt":S.num_heads="n_heads",S.num_layers="n_layers",S.hidden_size="d_model";break;case"exaone":S.num_heads="num_key_value_heads",S.num_layers="num_layers",S.dim_kv="head_dim",S.num_attention_heads="num_attention_heads";break;case"t5":case"mt5":case"longt5":S.num_decoder_layers="num_decoder_layers",S.num_decoder_heads="num_heads",S.decoder_dim_kv="d_kv",S.num_encoder_layers="num_layers",S.num_encoder_heads="num_heads",S.encoder_dim_kv="d_kv";break;case"bart":case"mbart":case"marian":case"whisper":case"m2m_100":case"blenderbot":case"blenderbot-small":case"florence2_language":S.num_decoder_layers="decoder_layers",S.num_decoder_heads="decoder_attention_heads",S.decoder_hidden_size="d_model",S.num_encoder_layers="encoder_layers",S.num_encoder_heads="encoder_attention_heads",S.encoder_hidden_size="d_model";break;case"speecht5":S.num_decoder_layers="decoder_layers",S.num_decoder_heads="decoder_attention_heads",S.decoder_hidden_size="hidden_size",S.num_encoder_layers="encoder_layers",S.num_encoder_heads="encoder_attention_heads",S.encoder_hidden_size="hidden_size";break;case"trocr":S.num_encoder_layers=S.num_decoder_layers="decoder_layers",S.num_encoder_heads=S.num_decoder_heads="decoder_attention_heads",S.encoder_hidden_size=S.decoder_hidden_size="d_model";break;case"musicgen_decoder":S.num_encoder_layers=S.num_decoder_layers="num_hidden_layers",S.num_encoder_heads=S.num_decoder_heads="num_attention_heads",S.encoder_hidden_size=S.decoder_hidden_size="hidden_size";break;case"moonshine":S.num_decoder_layers="decoder_num_hidden_layers",S.num_decoder_heads="decoder_num_key_value_heads",S.num_encoder_layers="encoder_num_hidden_layers",S.num_encoder_heads="encoder_num_key_value_heads",S.encoder_hidden_size=S.decoder_hidden_size="hidden_size";break;case"vision-encoder-decoder":const re=j(R.decoder),be="num_decoder_layers"in re,xe=(0,k.pick)(R,["model_type","is_encoder_decoder"]);return be?(xe.num_decoder_layers=re.num_decoder_layers,xe.num_decoder_heads=re.num_decoder_heads,xe.decoder_hidden_size=re.decoder_hidden_size,xe.num_encoder_layers=re.num_encoder_layers,xe.num_encoder_heads=re.num_encoder_heads,xe.encoder_hidden_size=re.encoder_hidden_size):(xe.num_layers=re.num_layers,xe.num_heads=re.num_heads,xe.hidden_size=re.hidden_size),xe}const H={...X,...(0,k.pick)(R,["model_type","multi_query","is_encoder_decoder"])};for(const re in S)H[re]=R[S[re]];return H}function O(R,{prefix:S="past_key_values",batch_size:X=1}={}){const H={},re=R.normalized_config;if(re.is_encoder_decoder&&"num_encoder_heads"in re&&"num_decoder_heads"in re){const be=re.encoder_dim_kv??re.encoder_hidden_size/re.num_encoder_heads,xe=re.decoder_dim_kv??re.decoder_hidden_size/re.num_decoder_heads,ye=[X,re.num_encoder_heads,0,be],se=[X,re.num_decoder_heads,0,xe];for(let ne=0;ne<re.num_decoder_layers;++ne)H[`${S}.${ne}.encoder.key`]=ye,H[`${S}.${ne}.encoder.value`]=ye,H[`${S}.${ne}.decoder.key`]=se,H[`${S}.${ne}.decoder.value`]=se}else{const be=re.num_heads,xe=re.num_layers,ye=re.dim_kv??re.hidden_size/(re.num_attention_heads??be);if(re.model_type==="falcon"){const se=[X*be,0,ye];for(let ne=0;ne<xe;++ne)H[`${S}.${ne}.key`]=se,H[`${S}.${ne}.value`]=se}else if(re.multi_query){const se=[X*be,0,2*ye];for(let ne=0;ne<xe;++ne)H[`${S}.${ne}.key_value`]=se}else if(re.model_type==="bloom"){const se=[X*be,ye,0],ne=[X*be,0,ye];for(let we=0;we<xe;++we)H[`${S}.${we}.key`]=se,H[`${S}.${we}.value`]=ne}else if(re.model_type==="openelm")for(let se=0;se<xe;++se){const ne=[X,be[se],0,ye];H[`${S}.${se}.key`]=ne,H[`${S}.${se}.value`]=ne}else{const se=[X,be,0,ye];for(let ne=0;ne<xe;++ne)H[`${S}.${ne}.key`]=se,H[`${S}.${ne}.value`]=se}}return H}class V{model_type=null;is_encoder_decoder=!1;max_position_embeddings;"transformers.js_config";constructor(S){Object.assign(this,S),this.normalized_config=j(this)}static async from_pretrained(S,{progress_callback:X=null,config:H=null,cache_dir:re=null,local_files_only:be=!1,revision:xe="main"}={}){!H||H instanceof V||(H=new V(H));const ye=H??await async function(se,ne){return await(0,P.getModelJSON)(se,"config.json",!0,ne)}(S,{progress_callback:X,config:H,cache_dir:re,local_files_only:be,revision:xe});return new this(ye)}}class q{static async from_pretrained(...S){return V.from_pretrained(...S)}}},"./src/env.js":(x,C,d)=>{d.r(C),d.d(C,{apis:()=>xe,env:()=>g});var k=d("?569f"),P=d("?3f59"),j=d("?154a");const O=typeof window<"u"&&window.document!==void 0,V=typeof self<"u"&&self.constructor?.name==="DedicatedWorkerGlobalScope",q=typeof self<"u"&&"caches"in self,R=typeof navigator<"u"&&"gpu"in navigator,S=typeof navigator<"u"&&"ml"in navigator,X=typeof process<"u",H=X&&process?.release?.name==="node",re=!b(k),be=!b(P),xe=Object.freeze({IS_BROWSER_ENV:O,IS_WEBWORKER_ENV:V,IS_WEB_CACHE_AVAILABLE:q,IS_WEBGPU_AVAILABLE:R,IS_WEBNN_AVAILABLE:S,IS_PROCESS_AVAILABLE:X,IS_NODE_ENV:H,IS_FS_AVAILABLE:re,IS_PATH_AVAILABLE:be}),ye=re&&be;let se="./";if(ye){const e=Object(import.meta).url;e?se=P.dirname(P.dirname(j.fileURLToPath(e))):typeof __dirname<"u"&&(se=P.dirname(__dirname))}const ne=ye?P.join(se,"/.cache/"):null,we="/models/",g={version:"3.3.3",backends:{onnx:{}},allowRemoteModels:!0,remoteHost:"https://huggingface.co/",remotePathTemplate:"{model}/resolve/{revision}/",allowLocalModels:!(O||V),localModelPath:ye?P.join(se,we):we,useFS:re,useBrowserCache:q,useFSCache:re,cacheDir:ne,useCustomCache:!1,customCache:null};function b(e){return Object.keys(e).length===0}},"./src/generation/configuration_utils.js":(x,C,d)=>{d.r(C),d.d(C,{GenerationConfig:()=>P});var k=d("./src/utils/core.js");class P{max_length=20;max_new_tokens=null;min_length=0;min_new_tokens=null;early_stopping=!1;max_time=null;do_sample=!1;num_beams=1;num_beam_groups=1;penalty_alpha=null;use_cache=!0;temperature=1;top_k=50;top_p=1;typical_p=1;epsilon_cutoff=0;eta_cutoff=0;diversity_penalty=0;repetition_penalty=1;encoder_repetition_penalty=1;length_penalty=1;no_repeat_ngram_size=0;bad_words_ids=null;force_words_ids=null;renormalize_logits=!1;constraints=null;forced_bos_token_id=null;forced_eos_token_id=null;remove_invalid_values=!1;exponential_decay_length_penalty=null;suppress_tokens=null;streamer=null;begin_suppress_tokens=null;forced_decoder_ids=null;guidance_scale=null;num_return_sequences=1;output_attentions=!1;output_hidden_states=!1;output_scores=!1;return_dict_in_generate=!1;pad_token_id=null;bos_token_id=null;eos_token_id=null;encoder_no_repeat_ngram_size=0;decoder_start_token_id=null;generation_kwargs={};constructor(O){Object.assign(this,(0,k.pick)(O,Object.getOwnPropertyNames(this)))}}},"./src/generation/logits_process.js":(x,C,d)=>{d.r(C),d.d(C,{ClassifierFreeGuidanceLogitsProcessor:()=>se,ForcedBOSTokenLogitsProcessor:()=>q,ForcedEOSTokenLogitsProcessor:()=>R,LogitsProcessor:()=>j,LogitsProcessorList:()=>V,LogitsWarper:()=>O,MinLengthLogitsProcessor:()=>be,MinNewTokensLengthLogitsProcessor:()=>xe,NoBadWordsLogitsProcessor:()=>ye,NoRepeatNGramLogitsProcessor:()=>H,RepetitionPenaltyLogitsProcessor:()=>re,SuppressTokensAtBeginLogitsProcessor:()=>S,TemperatureLogitsWarper:()=>ne,TopKLogitsWarper:()=>g,TopPLogitsWarper:()=>we,WhisperTimeStampLogitsProcessor:()=>X});var k=d("./src/utils/generic.js");d("./src/utils/tensor.js");var P=d("./src/utils/maths.js");class j extends k.Callable{_call(e,l){throw Error("`_call` should be implemented in a subclass")}}class O extends k.Callable{_call(e,l){throw Error("`_call` should be implemented in a subclass")}}class V extends k.Callable{constructor(){super(),this.processors=[]}push(e){this.processors.push(e)}extend(e){this.processors.push(...e)}_call(e,l){let M=l;for(const ue of this.processors)M=ue(e,M);return M}[Symbol.iterator](){return this.processors.values()}}class q extends j{constructor(e){super(),this.bos_token_id=e}_call(e,l){for(let M=0;M<e.length;++M)if(e[M].length===1){const ue=l[M].data;ue.fill(-1/0),ue[this.bos_token_id]=0}return l}}class R extends j{constructor(e,l){super(),this.max_length=e,this.eos_token_id=Array.isArray(l)?l:[l]}_call(e,l){for(let M=0;M<e.length;++M)if(e[M].length===this.max_length-1){const ue=l[M].data;ue.fill(-1/0);for(const D of this.eos_token_id)ue[D]=0}return l}}class S extends j{constructor(e,l){super(),this.begin_suppress_tokens=e,this.begin_index=l}_call(e,l){for(let M=0;M<e.length;++M)if(e[M].length===this.begin_index){const ue=l[M].data;for(const D of this.begin_suppress_tokens)ue[D]=-1/0}return l}}class X extends j{constructor(e,l){super(),this.eos_token_id=Array.isArray(e.eos_token_id)?e.eos_token_id[0]:e.eos_token_id,this.no_timestamps_token_id=e.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=l.length,l.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=e.max_initial_timestamp_index}_call(e,l){for(let M=0;M<e.length;++M){const ue=l[M].data;if(ue[this.no_timestamps_token_id]=-1/0,e[M].length===this.begin_index-1){ue.fill(-1/0),ue[this.timestamp_begin]=0;continue}const D=e[M].slice(this.begin_index),z=D.length>=1&&D[D.length-1]>=this.timestamp_begin,Re=D.length<2||D[D.length-2]>=this.timestamp_begin;if(z&&(Re?ue.subarray(this.timestamp_begin).fill(-1/0):ue.subarray(0,this.eos_token_id).fill(-1/0)),e[M].length===this.begin_index&&this.max_initial_timestamp_index!==null){const Je=this.timestamp_begin+this.max_initial_timestamp_index;ue.subarray(Je+1).fill(-1/0)}const Ne=(0,P.log_softmax)(ue);Math.log(Ne.subarray(this.timestamp_begin).map(Math.exp).reduce((Je,AA)=>Je+AA))>(0,P.max)(Ne.subarray(0,this.timestamp_begin))[0]&&ue.subarray(0,this.timestamp_begin).fill(-1/0)}return l}}class H extends j{constructor(e){super(),this.no_repeat_ngram_size=e}getNgrams(e){const l=e.length,M=[];for(let D=0;D<l+1-this.no_repeat_ngram_size;++D){const z=[];for(let Re=0;Re<this.no_repeat_ngram_size;++Re)z.push(e[D+Re]);M.push(z.map(Number))}const ue=new Map;for(const D of M){const z=D.slice(0,D.length-1),Re=JSON.stringify(z),Ne=ue.get(Re)??[];Ne.push(D[D.length-1]),ue.set(Re,Ne)}return ue}getGeneratedNgrams(e,l){const M=l.slice(l.length+1-this.no_repeat_ngram_size,l.length);return e.get(JSON.stringify(M.map(Number)))??[]}calcBannedNgramTokens(e){const l=[];if(e.length+1<this.no_repeat_ngram_size)return l;{const M=this.getNgrams(e);return this.getGeneratedNgrams(M,e)}}_call(e,l){for(let M=0;M<e.length;++M){const ue=l[M].data,D=this.calcBannedNgramTokens(e[M]);for(const z of D)ue[z]=-1/0}return l}}class re extends j{constructor(e){super(),this.penalty=e}_call(e,l){for(let M=0;M<e.length;++M){const ue=l[M].data;for(const D of new Set(e[M])){const z=Number(D);ue[z]<0?ue[z]*=this.penalty:ue[z]/=this.penalty}}return l}}class be extends j{constructor(e,l){super(),this.min_length=e,this.eos_token_id=Array.isArray(l)?l:[l]}_call(e,l){for(let M=0;M<e.length;++M)if(e[M].length<this.min_length){const ue=l[M].data;for(const D of this.eos_token_id)ue[D]=-1/0}return l}}class xe extends j{constructor(e,l,M){super(),this.prompt_length_to_skip=e,this.min_new_tokens=l,this.eos_token_id=Array.isArray(M)?M:[M]}_call(e,l){for(let M=0;M<e.length;++M)if(e[M].length-this.prompt_length_to_skip<this.min_new_tokens){const ue=l[M].data;for(const D of this.eos_token_id)ue[D]=-1/0}return l}}class ye extends j{constructor(e,l){super(),this.bad_words_ids=e,this.eos_token_id=Array.isArray(l)?l:[l]}_call(e,l){for(let M=0;M<e.length;++M){const ue=l[M].data,D=e[M];for(const z of this.bad_words_ids){let Re=!0;for(let Ne=1;Ne<=z.length-1&&z.length<D.length;++Ne)if(z.at(-Ne-1)!=D.at(-Ne)){Re=!1;break}Re&&(ue[z.at(-1)]=-1/0)}}return l}}class se extends j{constructor(e){if(super(),e<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${e}.`);this.guidance_scale=e}_call(e,l){if(l.dims[0]!==2*e.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got batch size ${l.dims[0]} for the logits and ${e.length} for the input ids.`);const M=e.length,ue=l.slice([0,M],null),D=l.slice([M,l.dims[0]],null);for(let z=0;z<D.data.length;++z)D.data[z]+=(ue.data[z]-D.data[z])*this.guidance_scale;return D}}class ne extends O{constructor(e){super(),this.temperature=e}_call(e,l){const M=l.data;for(let ue=0;ue<M.length;++ue)M[ue]/=this.temperature;return l}}class we extends O{constructor(e,{filter_value:l=-1/0,min_tokens_to_keep:M=1}={}){if(super(),e<0||e>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${e}`);if(!Number.isInteger(M)||M<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${M}`);this.top_p=e,this.filter_value=l,this.min_tokens_to_keep=M}}class g extends O{constructor(e,{filter_value:l=-1/0,min_tokens_to_keep:M=1}={}){if(super(),!Number.isInteger(e)||e<0)throw new Error(`\`top_k\` must be a positive integer, but is ${e}`);this.top_k=Math.max(e,M),this.filter_value=l}}},"./src/generation/logits_sampler.js":(x,C,d)=>{d.r(C),d.d(C,{LogitsSampler:()=>O});var k=d("./src/utils/generic.js"),P=d("./src/utils/tensor.js"),j=d("./src/utils/maths.js");d("./src/generation/configuration_utils.js");class O extends k.Callable{constructor(X){super(),this.generation_config=X}async _call(X){return this.sample(X)}async sample(X){throw Error("sample should be implemented in subclasses.")}getLogits(X,H){let re=X.dims.at(-1),be=X.data;if(H===-1)be=be.slice(-re);else{let xe=H*re;be=be.slice(xe,xe+re)}return be}randomSelect(X){let H=0;for(let be=0;be<X.length;++be)H+=X[be];let re=Math.random()*H;for(let be=0;be<X.length;++be)if(re-=X[be],re<=0)return be;return 0}static getSampler(X){if(X.do_sample)return new q(X);if(X.num_beams>1)return new R(X);if(X.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${X.num_return_sequences}.`);return new V(X)}}class V extends O{async sample(X){const H=(0,j.max)(X.data)[1];return[[BigInt(H),0]]}}class q extends O{async sample(X){let H=X.dims.at(-1);this.generation_config.top_k>0&&(H=Math.min(this.generation_config.top_k,H));const[re,be]=await(0,P.topk)(X,H),xe=(0,j.softmax)(re.data);return Array.from({length:this.generation_config.num_beams},()=>{const ye=this.randomSelect(xe);return[be.data[ye],Math.log(xe[ye])]})}}class R extends O{async sample(X){let H=X.dims.at(-1);this.generation_config.top_k>0&&(H=Math.min(this.generation_config.top_k,H));const[re,be]=await(0,P.topk)(X,H),xe=(0,j.softmax)(re.data);return Array.from({length:this.generation_config.num_beams},(ye,se)=>[be.data[se],Math.log(xe[se])])}}},"./src/generation/stopping_criteria.js":(x,C,d)=>{d.r(C),d.d(C,{EosTokenCriteria:()=>V,InterruptableStoppingCriteria:()=>q,MaxLengthCriteria:()=>O,StoppingCriteria:()=>P,StoppingCriteriaList:()=>j});var k=d("./src/utils/generic.js");class P extends k.Callable{_call(S,X){throw Error("StoppingCriteria needs to be subclassed")}}class j extends k.Callable{constructor(){super(),this.criteria=[]}push(S){this.criteria.push(S)}extend(S){S instanceof j?S=S.criteria:S instanceof P&&(S=[S]),this.criteria.push(...S)}_call(S,X){const H=new Array(S.length).fill(!1);for(const re of this.criteria){const be=re(S,X);for(let xe=0;xe<H.length;++xe)H[xe]||=be[xe]}return H}[Symbol.iterator](){return this.criteria.values()}}class O extends P{constructor(S,X=null){super(),this.max_length=S,this.max_position_embeddings=X}_call(S){return S.map(X=>X.length>=this.max_length)}}class V extends P{constructor(S){super(),Array.isArray(S)||(S=[S]),this.eos_token_id=S}_call(S,X){return S.map(H=>{const re=H.at(-1);return this.eos_token_id.some(be=>re==be)})}}class q extends P{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(S,X){return new Array(S.length).fill(this.interrupted)}}},"./src/generation/streamers.js":(x,C,d)=>{d.r(C),d.d(C,{BaseStreamer:()=>O,TextStreamer:()=>q,WhisperTextStreamer:()=>R});var k=d("./src/utils/core.js"),P=d("./src/tokenizers.js"),j=d("./src/env.js");class O{put(X){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const V=j.apis.IS_PROCESS_AVAILABLE?S=>process.stdout.write(S):S=>console.log(S);class q extends O{constructor(X,{skip_prompt:H=!1,callback_function:re=null,token_callback_function:be=null,skip_special_tokens:xe=!0,decode_kwargs:ye={},...se}={}){super(),this.tokenizer=X,this.skip_prompt=H,this.callback_function=re??V,this.token_callback_function=be,this.decode_kwargs={skip_special_tokens:xe,...ye,...se},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(X){if(X.length>1)throw Error("TextStreamer only supports batch size of 1");if(this.skip_prompt&&this.next_tokens_are_prompt)return void(this.next_tokens_are_prompt=!1);const H=X[0];this.token_callback_function?.(H),this.token_cache=(0,k.mergeArrays)(this.token_cache,H);const re=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let be;re.endsWith(`
`)?(be=re.slice(this.print_len),this.token_cache=[],this.print_len=0):re.length>0&&(0,P.is_chinese_char)(re.charCodeAt(re.length-1))?(be=re.slice(this.print_len),this.print_len+=be.length):(be=re.slice(this.print_len,re.lastIndexOf(" ")+1),this.print_len+=be.length),this.on_finalized_text(be,!1)}end(){let X;this.token_cache.length>0?(X=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):X="",this.next_tokens_are_prompt=!0,this.on_finalized_text(X,!0)}on_finalized_text(X,H){X.length>0&&this.callback_function?.(X),H&&this.callback_function===V&&j.apis.IS_PROCESS_AVAILABLE&&this.callback_function?.(`
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The following inputs will be ignored: "${_A.join(", ")}".`)}return uA}(K,U);try{const eA=Object.fromEntries(Object.entries(Ee).map(([uA,FA])=>[uA,FA.ort_tensor]));let sA=await K.run(eA);return sA=Ie(sA),sA}catch(eA){const sA=Object.fromEntries(Object.entries(Ee).map(([uA,{type:FA,dims:XA,data:WA}])=>[uA,{type:FA,dims:XA,data:WA}]));throw console.error(`An error occurred during model execution: "${eA}".`),console.error("Inputs given to model:",sA),eA}}function Ie(K){for(let U in K)(0,P.isONNXTensor)(K[U])?K[U]=new H.Tensor(K[U]):typeof K[U]=="object"&&Ie(K[U]);return K}function ze(K){if(K instanceof H.Tensor)return K;if(K.length===0)throw Error("items must be non-empty");if(Array.isArray(K[0])){if(K.some(U=>U.length!==K[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new H.Tensor("int64",BigInt64Array.from(K.flat().map(U=>BigInt(U))),[K.length,K[0].length])}return new H.Tensor("int64",BigInt64Array.from(K.map(U=>BigInt(U))),[1,K.length])}function Ue(K){return new H.Tensor("bool",[K],[1])}async function Pe(K,U){let{encoder_outputs:Ee,input_ids:eA,decoder_input_ids:sA,...uA}=U;if(!Ee){const FA=(0,V.pick)(U,K.sessions.model.inputNames);Ee=(await kA(K,FA)).last_hidden_state}return uA.input_ids=sA,uA.encoder_hidden_states=Ee,K.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(uA.encoder_attention_mask=U.attention_mask),await aA(K,uA,!0)}async function kA(K,U){const Ee=K.sessions.model,eA=(0,V.pick)(U,Ee.inputNames);if(Ee.inputNames.includes("inputs_embeds")&&!eA.inputs_embeds){if(!U.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");eA.inputs_embeds=await K.encode_text({input_ids:U.input_ids})}if(Ee.inputNames.includes("token_type_ids")&&!eA.token_type_ids){if(!eA.input_ids)throw new Error("Both `input_ids` and `token_type_ids` are missing in the model inputs.");eA.token_type_ids=(0,H.zeros_like)(eA.input_ids)}if(Ee.inputNames.includes("pixel_mask")&&!eA.pixel_mask){if(!eA.pixel_values)throw new Error("Both `pixel_values` and `pixel_mask` are missing in the model inputs.");const sA=eA.pixel_values.dims;eA.pixel_mask=(0,H.ones)([sA[0],sA[2],sA[3]])}return await Ge(Ee,eA)}async function aA(K,U,Ee=!1){const eA=K.sessions[Ee?"decoder_model_merged":"model"],{past_key_values:sA,...uA}=U;if(eA.inputNames.includes("use_cache_branch")&&(uA.use_cache_branch=Ue(!!sA)),eA.inputNames.includes("position_ids")&&uA.attention_mask&&!uA.position_ids){const XA=K.config.model_type==="paligemma"?1:0;uA.position_ids=function(WA,_A=null,ft=0){const{input_ids:gt,inputs_embeds:zt,attention_mask:Qt}=WA,{data:or,dims:Bt}=nA(Qt,ft);let Ot=new H.Tensor("int64",or,Bt);if(_A){const Ir=-(gt??zt).dims.at(1);Ot=Ot.slice(null,[Ir,null])}return Ot}(uA,sA,XA)}K.addPastKeyValues(uA,sA);const FA=(0,V.pick)(uA,eA.inputNames);return await Ge(eA,FA)}function iA({image_token_id:K,inputs_embeds:U,image_features:Ee,input_ids:eA,attention_mask:sA}){const uA=eA.tolist().map(_A=>_A.reduce((ft,gt,zt)=>(gt==K&&ft.push(zt),ft),[])),FA=uA.reduce((_A,ft)=>_A+ft.length,0),XA=Ee.dims[0];if(FA!==XA)throw new Error(`Image features and image tokens do not match: tokens: ${FA}, features ${XA}`);let WA=0;for(let _A=0;_A<uA.length;++_A){const ft=uA[_A],gt=U[_A];for(let zt=0;zt<ft.length;++zt)gt[ft[zt]].data.set(Ee[WA++].data)}return{inputs_embeds:U,attention_mask:sA}}async function yA(K,{input_ids:U=null,attention_mask:Ee=null,pixel_values:eA=null,position_ids:sA=null,inputs_embeds:uA=null,past_key_values:FA=null,generation_config:XA=null,logits_processor:WA=null,..._A}){if(!uA){if(uA=await K.encode_text({input_ids:U,..._A}),eA&&U.dims[1]!==1){const ft=await K.encode_image({pixel_values:eA,..._A});({inputs_embeds:uA,attention_mask:Ee}=K._merge_input_ids_with_image_features({image_features:ft,inputs_embeds:uA,input_ids:U,attention_mask:Ee}))}else if(FA&&eA&&U.dims[1]===1){const ft=U.dims[1],gt=Object.values(FA)[0].dims.at(-2);Ee=(0,H.cat)([(0,H.ones)([U.dims[0],gt]),Ee.slice(null,[Ee.dims[1]-ft,Ee.dims[1]])],1)}}if(!sA&&K.config.model_type==="qwen2_vl"){const{image_grid_thw:ft,video_grid_thw:gt}=_A;[sA]=K.get_rope_index(U,ft,gt,Ee)}return await aA(K,{inputs_embeds:uA,past_key_values:FA,attention_mask:Ee,position_ids:sA,generation_config:XA,logits_processor:WA},!0)}function nA(K,U=0){const[Ee,eA]=K.dims,sA=K.data,uA=new BigInt64Array(sA.length);for(let FA=0;FA<Ee;++FA){const XA=FA*eA;let WA=BigInt(U);for(let _A=0;_A<eA;++_A){const ft=XA+_A;sA[ft]===0n?uA[ft]=BigInt(1):(uA[ft]=WA,WA+=sA[ft])}}return{data:uA,dims:K.dims}}function Fe(K,U,Ee,eA){if(Ee.past_key_values){const sA=Object.values(Ee.past_key_values)[0].dims.at(-2),{input_ids:uA,attention_mask:FA}=Ee;if(!(FA&&FA.dims[1]>uA.dims[1])){if(sA<uA.dims[1])Ee.input_ids=uA.slice(null,[sA,null]);else if(K.config.image_token_index!=null&&uA.data.some(XA=>XA==K.config.image_token_index)){const XA=K.config.num_image_tokens;if(!XA)throw new Error("`num_image_tokens` is missing in the model configuration.");const WA=uA.dims[1]-(sA-XA);Ee.input_ids=uA.slice(null,[-WA,null]),Ee.attention_mask=(0,H.ones)([1,sA+WA])}}}return Ee}function Ze(K,U,Ee,eA){return Ee.past_key_values&&(U=U.map(sA=>[sA.at(-1)])),{...Ee,decoder_input_ids:ze(U)}}function mA(K,...U){return K.config.is_encoder_decoder?Ze(K,...U):Fe(K,...U)}function CA(K,U,Ee,eA){const sA=!!Ee.past_key_values;return eA.guidance_scale!==null&&eA.guidance_scale>1&&(sA?Ee.input_ids=(0,H.cat)([Ee.input_ids,Ee.input_ids],0):(Ee.input_ids=(0,H.cat)([Ee.input_ids,(0,H.full_like)(Ee.input_ids,BigInt(eA.pad_token_id))],0),Ee.attention_mask=(0,H.cat)([Ee.attention_mask,(0,H.full_like)(Ee.attention_mask,0n)],0))),!sA&&Ee.pixel_values||(Ee.pixel_values=(0,H.full)([0,0,3,384,384],1)),sA&&(Ee.images_seq_mask=new H.Tensor("bool",new Array(1).fill(!0).fill(!1,0,1),[1,1]),Ee.images_emb_mask=new H.Tensor("bool",new Array(0).fill(!1),[1,1,0])),Ee}class Te extends O.Callable{main_input_name="input_ids";forward_params=["input_ids","attention_mask"];constructor(U,Ee,eA){super(),this.config=U,this.sessions=Ee,this.configs=eA;const sA=AA.get(this.constructor),uA=Ye.get(sA);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,uA){case M:this.can_generate=!0,this._forward=aA,this._prepare_inputs_for_generation=Fe;break;case e:case l:case z:this.can_generate=!0,this._forward=Pe,this._prepare_inputs_for_generation=Ze;break;case b:this._forward=Pe;break;case D:this.can_generate=!0,this._forward=yA,this._prepare_inputs_for_generation=mA;break;case Ne:this.can_generate=!0,this._prepare_inputs_for_generation=mA;break;case Re:this.can_generate=!0,this._prepare_inputs_for_generation=CA;break;default:this._forward=kA}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){const U=[];for(const Ee of Object.values(this.sessions))Ee?.handler?.dispose&&U.push(Ee.handler.dispose());return await Promise.all(U)}static async from_pretrained(U,{progress_callback:Ee=null,config:eA=null,cache_dir:sA=null,local_files_only:uA=!1,revision:FA="main",model_file_name:XA=null,subfolder:WA="onnx",device:_A=null,dtype:ft=null,use_external_data_format:gt=null,session_options:zt={}}={}){let Qt={progress_callback:Ee,config:eA,cache_dir:sA,local_files_only:uA,revision:FA,model_file_name:XA,subfolder:WA,device:_A,dtype:ft,use_external_data_format:gt,session_options:zt};const or=AA.get(this),Bt=Ye.get(or);let Ot;if(eA=Qt.config=await k.AutoConfig.from_pretrained(U,Qt),Bt===M)Ot=await Promise.all([xA(U,{model:Qt.model_file_name??"model"},Qt),GA(U,{generation_config:"generation_config.json"},Qt)]);else if(Bt===e||Bt===l)Ot=await Promise.all([xA(U,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},Qt),GA(U,{generation_config:"generation_config.json"},Qt)]);else if(Bt===ue)Ot=await Promise.all([xA(U,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},Qt)]);else if(Bt===b)Ot=await Promise.all([xA(U,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},Qt)]);else if(Bt===D){const Ir={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};eA.is_encoder_decoder&&(Ir.model="encoder_model"),Ot=await Promise.all([xA(U,Ir,Qt),GA(U,{generation_config:"generation_config.json"},Qt)])}else if(Bt===z)Ot=await Promise.all([xA(U,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},Qt),GA(U,{generation_config:"generation_config.json"},Qt)]);else if(Bt===Re)Ot=await Promise.all([xA(U,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"},Qt),GA(U,{generation_config:"generation_config.json"},Qt)]);else if(Bt===Ne)Ot=await Promise.all([xA(U,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"},Qt),GA(U,{generation_config:"generation_config.json"},Qt)]);else{if(Bt!==g){const Ir=or??eA?.model_type;Ir!=="custom"&&console.warn(`Model type for '${Ir}' not found, assuming encoder-only architecture. Please report this at ${R.GITHUB_ISSUE_URL}.`)}Ot=await Promise.all([xA(U,{model:Qt.model_file_name??"model"},Qt)])}return new this(eA,...Ot)}async _call(U){return await this.forward(U)}async forward(U){return await this._forward(this,U)}get generation_config(){return this.configs?.generation_config??null}_get_logits_warper(U){const Ee=new S.LogitsProcessorList;return U.temperature!==null&&U.temperature!==1&&Ee.push(new S.TemperatureLogitsWarper(U.temperature)),U.top_k!==null&&U.top_k!==0&&Ee.push(new S.TopKLogitsWarper(U.top_k)),U.top_p!==null&&U.top_p<1&&Ee.push(new S.TopPLogitsWarper(U.top_p)),Ee}_get_logits_processor(U,Ee,eA=null){const sA=new S.LogitsProcessorList;if(U.repetition_penalty!==null&&U.repetition_penalty!==1&&sA.push(new S.RepetitionPenaltyLogitsProcessor(U.repetition_penalty)),U.no_repeat_ngram_size!==null&&U.no_repeat_ngram_size>0&&sA.push(new S.NoRepeatNGramLogitsProcessor(U.no_repeat_ngram_size)),U.bad_words_ids!==null&&sA.push(new S.NoBadWordsLogitsProcessor(U.bad_words_ids,U.eos_token_id)),U.min_length!==null&&U.eos_token_id!==null&&U.min_length>0&&sA.push(new S.MinLengthLogitsProcessor(U.min_length,U.eos_token_id)),U.min_new_tokens!==null&&U.eos_token_id!==null&&U.min_new_tokens>0&&sA.push(new S.MinNewTokensLengthLogitsProcessor(Ee,U.min_new_tokens,U.eos_token_id)),U.forced_bos_token_id!==null&&sA.push(new S.ForcedBOSTokenLogitsProcessor(U.forced_bos_token_id)),U.forced_eos_token_id!==null&&sA.push(new S.ForcedEOSTokenLogitsProcessor(U.max_length,U.forced_eos_token_id)),U.begin_suppress_tokens!==null){const uA=Ee>1||U.forced_bos_token_id===null?Ee:Ee+1;sA.push(new S.SuppressTokensAtBeginLogitsProcessor(U.begin_suppress_tokens,uA))}return U.guidance_scale!==null&&U.guidance_scale>1&&sA.push(new S.ClassifierFreeGuidanceLogitsProcessor(U.guidance_scale)),eA!==null&&sA.extend(eA),sA}_prepare_generation_config(U,Ee,eA=X.GenerationConfig){const sA={...this.config};for(const FA of["decoder","generator","text_config"])FA in sA&&Object.assign(sA,sA[FA]);const uA=new eA(sA);return Object.assign(uA,this.generation_config??{}),U&&Object.assign(uA,U),Ee&&Object.assign(uA,(0,V.pick)(Ee,Object.getOwnPropertyNames(uA))),uA}_get_stopping_criteria(U,Ee=null){const eA=new xe.StoppingCriteriaList;return U.max_length!==null&&eA.push(new xe.MaxLengthCriteria(U.max_length,this.config.max_position_embeddings??null)),U.eos_token_id!==null&&eA.push(new xe.EosTokenCriteria(U.eos_token_id)),Ee&&eA.extend(Ee),eA}_validate_model_class(){if(!this.can_generate){const U=[Ho,Ro,Yo,Oo],Ee=AA.get(this.constructor),eA=new Set,sA=this.config.model_type;for(const FA of U){const XA=FA.get(sA);XA&&eA.add(XA[0])}let uA=`The current model class (${Ee}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw eA.size>0&&(uA+=` Please use the following class instead: ${[...eA].join(", ")}`),Error(uA)}}prepare_inputs_for_generation(...U){return this._prepare_inputs_for_generation(this,...U)}_update_model_kwargs_for_generation({generated_input_ids:U,outputs:Ee,model_inputs:eA,is_encoder_decoder:sA}){return eA.past_key_values=this.getPastKeyValues(Ee,eA.past_key_values),eA.input_ids=new H.Tensor("int64",U.flat(),[U.length,1]),sA||(eA.attention_mask=(0,H.cat)([eA.attention_mask,(0,H.ones)([eA.attention_mask.dims[0],1])],1)),eA.position_ids=null,eA}_prepare_model_inputs({inputs:U,bos_token_id:Ee,model_kwargs:eA}){const sA=(0,V.pick)(eA,this.forward_params),uA=this.main_input_name;if(uA in sA){if(U)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else sA[uA]=U;return{inputs_tensor:sA[uA],model_inputs:sA,model_input_name:uA}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:U,model_inputs:Ee,model_input_name:eA,generation_config:sA}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!Ee.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:FA,pixel_values:XA,attention_mask:WA,..._A}=Ee,ft=await this._prepare_inputs_embeds(Ee);Ee={..._A,...(0,V.pick)(ft,["inputs_embeds","attention_mask"])}}let{last_hidden_state:uA}=await kA(this,Ee);if(sA.guidance_scale!==null&&sA.guidance_scale>1)uA=(0,H.cat)([uA,(0,H.full_like)(uA,0)],0),"attention_mask"in Ee&&(Ee.attention_mask=(0,H.cat)([Ee.attention_mask,(0,H.zeros_like)(Ee.attention_mask)],0));else if(Ee.decoder_input_ids){const FA=ze(Ee.decoder_input_ids).dims[0];if(FA!==uA.dims[0]){if(uA.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${uA.dims[0]}) than the decoder inputs (${FA}).`);uA=(0,H.cat)(Array.from({length:FA},()=>uA),0)}}return Ee.encoder_outputs=uA,Ee}_prepare_decoder_input_ids_for_generation({batch_size:U,model_input_name:Ee,model_kwargs:eA,decoder_start_token_id:sA,bos_token_id:uA,generation_config:FA}){let{decoder_input_ids:XA,...WA}=eA;if(!(XA instanceof H.Tensor)){if(XA)Array.isArray(XA[0])||(XA=Array.from({length:U},()=>XA));else if(sA??=uA,this.config.model_type==="musicgen")XA=Array.from({length:U*this.config.decoder.num_codebooks},()=>[sA]);else if(Array.isArray(sA)){if(sA.length!==U)throw new Error(`\`decoder_start_token_id\` expcted to have length ${U} but got ${sA.length}`);XA=sA}else XA=Array.from({length:U},()=>[sA]);XA=ze(XA)}return eA.decoder_attention_mask=(0,H.ones_like)(XA),{input_ids:XA,model_inputs:WA}}async generate({inputs:U=null,generation_config:Ee=null,logits_processor:eA=null,stopping_criteria:sA=null,streamer:uA=null,...FA}){this._validate_model_class(),Ee=this._prepare_generation_config(Ee,FA);let{inputs_tensor:XA,model_inputs:WA,model_input_name:_A}=this._prepare_model_inputs({inputs:U,model_kwargs:FA});const ft=this.config.is_encoder_decoder;let gt;ft&&("encoder_outputs"in WA||(WA=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:XA,model_inputs:WA,model_input_name:_A,generation_config:Ee}))),ft?{input_ids:gt,model_inputs:WA}=this._prepare_decoder_input_ids_for_generation({batch_size:WA[_A].dims.at(0),model_input_name:_A,model_kwargs:WA,decoder_start_token_id:Ee.decoder_start_token_id,bos_token_id:Ee.bos_token_id,generation_config:Ee}):gt=WA[_A];let zt=gt.dims.at(-1);Ee.max_new_tokens!==null&&(Ee.max_length=zt+Ee.max_new_tokens);const Qt=this._get_logits_processor(Ee,zt,eA),or=this._get_stopping_criteria(Ee,sA),Bt=WA[_A].dims.at(0),Ot=ye.LogitsSampler.getSampler(Ee),Ir=new Array(Bt).fill(0),Mr=gt.tolist();let Vr;uA&&uA.put(Mr);let Jr={};for(;;){if(WA=this.prepare_inputs_for_generation(Mr,WA,Ee),Vr=await this.forward(WA),Ee.output_attentions&&Ee.return_dict_in_generate){const _t=this.getAttentions(Vr);for(const Ua in _t)Ua in Jr||(Jr[Ua]=[]),Jr[Ua].push(_t[Ua])}const Yr=Qt(Mr,Vr.logits.slice(null,-1,null)),Ca=[];for(let _t=0;_t<Yr.dims.at(0);++_t){const Ua=Yr[_t],rn=await Ot(Ua);for(const[$0,an]of rn){const qn=BigInt($0);Ir[_t]+=an,Mr[_t].push(qn),Ca.push([qn]);break}}if(uA&&uA.put(Ca),or(Mr).every(_t=>_t))break;WA=this._update_model_kwargs_for_generation({generated_input_ids:Ca,outputs:Vr,model_inputs:WA,is_encoder_decoder:ft})}uA&&uA.end();const Sa=this.getPastKeyValues(Vr,WA.past_key_values,!0),_r=new H.Tensor("int64",Mr.flat(),[Mr.length,Mr[0].length]);if(Ee.return_dict_in_generate)return{sequences:_r,past_key_values:Sa,...Jr};for(const Yr of Object.values(Vr))Yr.location==="gpu-buffer"&&Yr.dispose();return _r}getPastKeyValues(U,Ee,eA=!1){const sA=Object.create(null);for(const uA in U)if(uA.startsWith("present")){const FA=uA.replace("present","past_key_values"),XA=uA.includes("encoder");if(sA[FA]=XA&&Ee?Ee[FA]:U[uA],Ee&&(!XA||eA)){const WA=Ee[FA];WA.location==="gpu-buffer"&&WA.dispose()}}return sA}getAttentions(U){const Ee={};for(const eA of["cross_attentions","encoder_attentions","decoder_attentions"])for(const sA in U)sA.startsWith(eA)&&(eA in Ee||(Ee[eA]=[]),Ee[eA].push(U[sA]));return Ee}addPastKeyValues(U,Ee){if(Ee)Object.assign(U,Ee);else{const eA=this.sessions.decoder_model_merged??this.sessions.model,sA=eA?.config?.kv_cache_dtype??"float32",uA=sA==="float16"?new Uint16Array:[],FA=(U[this.main_input_name]??U.attention_mask)?.dims?.[0]??1,XA=(0,k.getKeyValueShapes)(this.config,{batch_size:FA});for(const WA in XA)U[WA]=new H.Tensor(sA,uA,XA[WA])}}async encode_image({pixel_values:U}){const Ee=(await Ge(this.sessions.vision_encoder,{pixel_values:U})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${Ee.dims[1]}).`),this.config.num_image_tokens=Ee.dims[1]),Ee}async encode_text({input_ids:U}){return(await Ge(this.sessions.embed_tokens,{input_ids:U})).inputs_embeds}}class pA{}class EA extends pA{constructor({last_hidden_state:U,hidden_states:Ee=null,attentions:eA=null}){super(),this.last_hidden_state=U,this.hidden_states=Ee,this.attentions=eA}}class SA extends Te{}class YA extends SA{}class J extends SA{async _call(U){return new Fa(await super._call(U))}}class OA extends SA{async _call(U){return new Nt(await super._call(U))}}class ge extends SA{async _call(U){return new Da(await super._call(U))}}class $ extends SA{async _call(U){return new ja(await super._call(U))}}class me extends Te{}class Se extends me{}class qe extends me{async _call(U){return new Fa(await super._call(U))}}class dA extends me{async _call(U){return new Nt(await super._call(U))}}class oA extends me{async _call(U){return new Da(await super._call(U))}}class BA extends Te{}class QA extends BA{}class TA extends Te{}class Mt extends TA{}class Tt extends TA{async _call(U){return new Fa(await super._call(U))}}class ut extends TA{async _call(U){return new Nt(await super._call(U))}}class Ct extends TA{async _call(U){return new Da(await super._call(U))}}class $t extends TA{async _call(U){return new ja(await super._call(U))}}class Qr extends Te{}class tr extends Qr{}class da extends Qr{async _call(U){return new Fa(await super._call(U))}}class bt extends Qr{async _call(U){return new Nt(await super._call(U))}}class Ka extends Qr{async _call(U){return new Da(await super._call(U))}}class Oa extends Qr{async _call(U){return new ja(await super._call(U))}}class Ia extends Te{}class ZA extends Ia{}class $A extends Ia{async _call(U){return new Fa(await super._call(U))}}class PA extends Ia{async _call(U){return new Nt(await super._call(U))}}class qt extends Ia{async _call(U){return new Da(await super._call(U))}}class cs extends Ia{async _call(U){return new ja(await super._call(U))}}class Za extends Te{}class zs extends Za{}class ws extends Za{async _call(U){return new Fa(await super._call(U))}}class ks extends Za{async _call(U){return new Nt(await super._call(U))}}class Fs extends Za{async _call(U){return new Da(await super._call(U))}}class $r extends Za{async _call(U){return new ja(await super._call(U))}}class xt extends Te{}class Ea extends xt{}class Tr extends xt{async _call(U){return new Fa(await super._call(U))}}class Hr extends xt{async _call(U){return new Nt(await super._call(U))}}class gs extends xt{async _call(U){return new Da(await super._call(U))}}class Kt extends xt{async _call(U){return new ja(await super._call(U))}}class Xa extends Te{}class us extends Xa{}class hi extends Xa{async _call(U){return new Fa(await super._call(U))}}class _s extends Xa{async _call(U){return new Nt(await super._call(U))}}class $s extends Xa{async _call(U){return new Da(await super._call(U))}}class mi extends Xa{async _call(U){return new ja(await super._call(U))}}class Ur extends Te{}class ts extends Ur{}class ds extends Ur{async _call(U){return new Nt(await super._call(U))}}class ei extends Ur{async _call(U){return new Da(await super._call(U))}}class qa extends Ur{async _call(U){return new ja(await super._call(U))}}class xs extends Ur{async _call(U){return new Fa(await super._call(U))}}class kr extends Te{}class Ss extends kr{}class Ns extends kr{async _call(U){return new Fa(await super._call(U))}}class Ci extends kr{async _call(U){return new Nt(await super._call(U))}}class Os extends kr{async _call(U){return new Da(await super._call(U))}}class Bs extends Te{}class Ls extends Bs{}class vs extends Bs{async _call(U){return new Fa(await super._call(U))}}class wt extends Bs{async _call(U){return new Nt(await super._call(U))}}class Pt extends Bs{async _call(U){return new ja(await super._call(U))}}class wa extends Te{}class Ai extends wa{}class ys extends wa{async _call(U){return new Fa(await super._call(U))}}class Ys extends wa{async _call(U){return new Nt(await super._call(U))}}class rs extends wa{async _call(U){return new Da(await super._call(U))}}class Hs extends wa{async _call(U){return new ja(await super._call(U))}}class tA extends Te{}class _ extends tA{}class Be extends tA{async _call(U){return new Fa(await super._call(U))}}class Oe extends tA{async _call(U){return new Nt(await super._call(U))}}class Ve extends tA{async _call(U){return new ja(await super._call(U))}}class $e extends Te{}class fA extends $e{}class NA extends $e{async _call(U){return new Nt(await super._call(U))}}class He extends $e{async _call(U){return new ja(await super._call(U))}}class VA extends $e{async _call(U){return new Fa(await super._call(U))}}class bA extends Te{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]}class Yt extends bA{}class Vt extends bA{}class cr extends Te{}class sr extends cr{}class jt extends cr{}class jA extends Te{}class KA extends jA{}class Jt extends jA{}class La extends Te{}class fa extends La{}class ir extends La{}class rt extends La{async _call(U){return new Nt(await super._call(U))}}class dr extends Te{}class gA extends dr{}class JA extends dr{}class pa extends dr{async _call(U){return new Nt(await super._call(U))}}class Nr extends dr{}class Rr extends Te{}class Pr extends Rr{}class Cr extends Rr{}class Ut extends Te{}class xr extends Ut{}class ti extends Ut{}class Ya extends Te{}class Fi extends Ya{}class Ma extends Ya{async _call(U){return new Fa(await super._call(U))}}class Si extends Ya{async _call(U){return new Nt(await super._call(U))}}class Gt extends Ya{async _call(U){return new Da(await super._call(U))}}class ea extends Ya{async _call(U){return new ja(await super._call(U))}}class Wa extends Te{}class s0 extends Wa{}class i0 extends Wa{async _call(U){return new Fa(await super._call(U))}}class n0 extends Wa{async _call(U){return new Nt(await super._call(U))}}class o0 extends Wa{async _call(U){return new Da(await super._call(U))}}class ri extends Wa{async _call(U){return new ja(await super._call(U))}}class Rs extends Te{}class l0 extends Rs{}class Ni extends Rs{async _call(U){return new Fa(await super._call(U))}}class Qa extends Rs{async _call(U){return new Nt(await super._call(U))}}class Kr extends Rs{async _call(U){return new Da(await super._call(U))}}class qA extends Rs{async _call(U){return new ja(await super._call(U))}}class Ga extends Te{}class dn extends Ga{}class Oi extends Ga{}class fn extends Te{requires_attention_mask=!1;main_input_name="input_features";forward_params=["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]}class c0 extends fn{}class g0 extends fn{_prepare_generation_config(U,Ee){return super._prepare_generation_config(U,Ee,ne.WhisperGenerationConfig)}_retrieve_init_tokens(U){const Ee=[U.decoder_start_token_id];let eA=U.language;const sA=U.task;if(U.is_multilingual){eA||(console.warn("No language specified - defaulting to English (en)."),eA="en");const uA=`<|${(0,we.whisper_language_to_code)(eA)}|>`;Ee.push(U.lang_to_id[uA]),Ee.push(U.task_to_id[sA??"transcribe"])}else if(eA||sA)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!U.return_timestamps&&U.no_timestamps_token_id&&Ee.at(-1)!==U.no_timestamps_token_id?Ee.push(U.no_timestamps_token_id):U.return_timestamps&&Ee.at(-1)===U.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),Ee.pop()),Ee.filter(uA=>uA!=null)}async generate({inputs:U=null,generation_config:Ee=null,logits_processor:eA=null,stopping_criteria:sA=null,...uA}){Ee=this._prepare_generation_config(Ee,uA);const FA=uA.decoder_input_ids??this._retrieve_init_tokens(Ee);if(Ee.return_timestamps&&(eA??=new S.LogitsProcessorList,eA.push(new S.WhisperTimeStampLogitsProcessor(Ee,FA))),Ee.begin_suppress_tokens&&(eA??=new S.LogitsProcessorList,eA.push(new S.SuppressTokensAtBeginLogitsProcessor(Ee.begin_suppress_tokens,FA.length))),Ee.return_token_timestamps){if(!Ee.alignment_heads)throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");Ee.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),Ee.output_attentions=!0,Ee.return_dict_in_generate=!0}const XA=await super.generate({inputs:U,generation_config:Ee,logits_processor:eA,decoder_input_ids:FA,...uA});return Ee.return_token_timestamps&&(XA.token_timestamps=this._extract_token_timestamps(XA,Ee.alignment_heads,Ee.num_frames)),XA}_extract_token_timestamps(U,Ee,eA=null,sA=.02){if(!U.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");eA==null&&console.warn("`num_frames` has not been set, meaning the entire audio will be analyzed. This may lead to inaccurate token-level timestamps for short audios (< 30 seconds).");let uA=this.config.median_filter_width;uA===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),uA=7);const FA=U.cross_attentions,XA=Array.from({length:this.config.decoder_layers},(Bt,Ot)=>(0,H.cat)(FA.map(Ir=>Ir[Ot]),2)),WA=(0,H.stack)(Ee.map(([Bt,Ot])=>{if(Bt>=XA.length)throw new Error(`Layer index ${Bt} is out of bounds for cross attentions (length ${XA.length}).`);return eA?XA[Bt].slice(null,Ot,null,[0,eA]):XA[Bt].slice(null,Ot)})).transpose(1,0,2,3),[_A,ft]=(0,H.std_mean)(WA,-2,0,!0),gt=WA.clone();for(let Bt=0;Bt<gt.dims[0];++Bt){const Ot=gt[Bt];for(let Ir=0;Ir<Ot.dims[0];++Ir){const Mr=Ot[Ir],Vr=_A[Bt][Ir][0].data,Jr=ft[Bt][Ir][0].data;for(let Sa=0;Sa<Mr.dims[0];++Sa){let _r=Mr[Sa].data;for(let Yr=0;Yr<_r.length;++Yr)_r[Yr]=(_r[Yr]-Jr[Yr])/Vr[Yr];_r.set((0,be.medianFilter)(_r,uA))}}}const zt=[(0,H.mean)(gt,1)],Qt=U.sequences.dims,or=new H.Tensor("float32",new Float32Array(Qt[0]*Qt[1]),Qt);for(let Bt=0;Bt<Qt[0];++Bt){const Ot=zt[Bt].neg().squeeze_(0),[Ir,Mr]=(0,be.dynamic_time_warping)(Ot.tolist()),Vr=Array.from({length:Ir.length-1},(_r,Yr)=>Ir[Yr+1]-Ir[Yr]),Jr=(0,V.mergeArrays)([1],Vr).map(_r=>!!_r),Sa=[];for(let _r=0;_r<Jr.length;++_r)Jr[_r]&&Sa.push(Mr[_r]*sA);or[Bt].data.set(Sa,1)}return or}}class pn extends Te{requires_attention_mask=!1;main_input_name="input_values";forward_params=["input_values","decoder_input_ids","past_key_values"]}class j0 extends pn{}class Ta extends pn{}class rr extends Te{main_input_name="pixel_values";forward_params=["pixel_values","decoder_input_ids","encoder_hidden_states","past_key_values"]}class bi extends Te{forward_params=["input_ids","attention_mask","pixel_values","position_ids","past_key_values"]}class Ii extends bi{_merge_input_ids_with_image_features({inputs_embeds:U,image_features:Ee,input_ids:eA,attention_mask:sA}){const uA=this.config.image_token_index,FA=eA.tolist().map(gt=>gt.findIndex(zt=>zt==uA)),XA=FA.every(gt=>gt===-1),WA=FA.every(gt=>gt!==-1);if(!XA&&!WA)throw new Error("Every input should contain either 0 or 1 image token.");if(XA)return{inputs_embeds:U,attention_mask:sA};const _A=[],ft=[];for(let gt=0;gt<FA.length;++gt){const zt=FA[gt],Qt=U[gt],or=Ee[gt],Bt=sA[gt];_A.push((0,H.cat)([Qt.slice([0,zt]),or,Qt.slice([zt+1,Qt.dims[0]])],0)),ft.push((0,H.cat)([Bt.slice([0,zt]),(0,H.ones)([or.dims[0]]),Bt.slice([zt+1,Bt.dims[0]])],0))}return{inputs_embeds:(0,H.stack)(_A,0),attention_mask:(0,H.stack)(ft,0)}}}class ka extends Ii{}class Ws extends Ii{}class as extends Te{forward_params=["input_ids","inputs_embeds","attention_mask","pixel_values","encoder_outputs","decoder_input_ids","decoder_inputs_embeds","decoder_attention_mask","past_key_values"];main_input_name="inputs_embeds"}class hn extends as{_merge_input_ids_with_image_features({inputs_embeds:U,image_features:Ee,input_ids:eA,attention_mask:sA}){return{inputs_embeds:(0,H.cat)([Ee,U],1),attention_mask:(0,H.cat)([(0,H.ones)(Ee.dims.slice(0,2)),sA],1)}}async _prepare_inputs_embeds({input_ids:U,pixel_values:Ee,inputs_embeds:eA,attention_mask:sA}){if(!U&&!Ee)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let uA,FA;return U&&(uA=await this.encode_text({input_ids:U})),Ee&&(FA=await this.encode_image({pixel_values:Ee})),uA&&FA?{inputs_embeds:eA,attention_mask:sA}=this._merge_input_ids_with_image_features({inputs_embeds:uA,image_features:FA,input_ids:U,attention_mask:sA}):eA=uA||FA,{inputs_embeds:eA,attention_mask:sA}}async forward({input_ids:U,pixel_values:Ee,attention_mask:eA,decoder_input_ids:sA,decoder_attention_mask:uA,encoder_outputs:FA,past_key_values:XA,inputs_embeds:WA,decoder_inputs_embeds:_A}){if(WA||({inputs_embeds:WA,attention_mask:eA}=await this._prepare_inputs_embeds({input_ids:U,pixel_values:Ee,inputs_embeds:WA,attention_mask:eA})),!FA){let{last_hidden_state:gt}=await kA(this,{inputs_embeds:WA,attention_mask:eA});FA=gt}if(!_A){if(!sA)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");_A=await this.encode_text({input_ids:sA})}return await aA(this,{inputs_embeds:_A,attention_mask:uA,encoder_attention_mask:eA,encoder_hidden_states:FA,past_key_values:XA},!0)}}class mn extends Te{forward_params=["input_ids","attention_mask","pixel_values","position_ids","past_key_values"]}class Vs extends mn{_merge_input_ids_with_image_features(U){const Ee=U.image_features.dims.at(-1),eA=U.image_features.view(-1,Ee);return iA({image_token_id:this.config.image_token_index,...U,image_features:eA})}}class Cn extends Te{forward_params=["input_ids","attention_mask","pixel_values","pixel_attention_mask","position_ids","past_key_values"]}class bn extends Cn{async encode_image({pixel_values:U,pixel_attention_mask:Ee}){return(await Ge(this.sessions.vision_encoder,{pixel_values:U,pixel_attention_mask:Ee})).image_features}_merge_input_ids_with_image_features(U){const Ee=U.image_features.dims.at(-1),eA=U.image_features.view(-1,Ee);return iA({image_token_id:this.config.image_token_id,...U,image_features:eA})}}class In extends Te{forward_params=["input_ids","inputs_embeds","attention_mask","position_ids","pixel_values","image_sizes","past_key_values"]}class DA extends In{async forward({input_ids:U=null,attention_mask:Ee=null,pixel_values:eA=null,image_sizes:sA=null,position_ids:uA=null,inputs_embeds:FA=null,past_key_values:XA=null,generation_config:WA=null,logits_processor:_A=null,...ft}){if(!FA){let gt;if(eA&&U.dims[1]!==1){if(!sA)throw new Error("`image_sizes` must be provided when `pixel_values` is provided.");({image_features:gt}=await Ge(this.sessions.vision_encoder,{pixel_values:eA,image_sizes:sA}))}else{const zt=this.config.normalized_config.hidden_size;gt=new H.Tensor("float32",[],[0,zt])}({inputs_embeds:FA}=await Ge(this.sessions.prepare_inputs_embeds,{input_ids:U,image_features:gt}))}return await aA(this,{inputs_embeds:FA,past_key_values:XA,attention_mask:Ee,position_ids:uA,generation_config:WA,logits_processor:_A},!1)}}class js extends Te{}class RA extends js{}class wn extends js{static async from_pretrained(U,Ee={}){return super.from_pretrained(U,{...Ee,model_file_name:Ee.model_file_name??"text_model"})}}class Li extends js{static async from_pretrained(U,Ee={}){return super.from_pretrained(U,{...Ee,model_file_name:Ee.model_file_name??"text_model"})}}class xa extends js{static async from_pretrained(U,Ee={}){return super.from_pretrained(U,{...Ee,model_file_name:Ee.model_file_name??"vision_model"})}}class Pa extends js{static async from_pretrained(U,Ee={}){return super.from_pretrained(U,{...Ee,model_file_name:Ee.model_file_name??"vision_model"})}}class ai extends Te{}class wi extends ai{}class kn extends ai{static async from_pretrained(U,Ee={}){return super.from_pretrained(U,{...Ee,model_file_name:Ee.model_file_name??"text_model"})}}class zr extends js{static async from_pretrained(U,Ee={}){return super.from_pretrained(U,{...Ee,model_file_name:Ee.model_file_name??"vision_model"})}}class u0 extends Te{}class d0 extends u0{}class Es extends Te{}class si extends Es{async forward(U){const Ee=!U.input_ids,eA=!U.pixel_values;if(Ee&&eA)throw new Error("Either `input_ids` or `pixel_values` should be provided.");if(Ee&&(U.input_ids=(0,H.ones)([U.pixel_values.dims[0],1])),eA){const{image_size:_A}=this.config.vision_config;U.pixel_values=(0,H.full)([0,3,_A,_A],0)}const{text_embeddings:sA,image_embeddings:uA,l2norm_text_embeddings:FA,l2norm_image_embeddings:XA}=await super.forward(U),WA={};return Ee||(WA.text_embeddings=sA,WA.l2norm_text_embeddings=FA),eA||(WA.image_embeddings=uA,WA.l2norm_image_embeddings=XA),WA}}class f0 extends Es{static async from_pretrained(U,Ee={}){return super.from_pretrained(U,{...Ee,model_file_name:Ee.model_file_name??"text_model"})}}class Yi extends Es{static async from_pretrained(U,Ee={}){return super.from_pretrained(U,{...Ee,model_file_name:Ee.model_file_name??"vision_model"})}}class ii extends Te{}class HA extends ii{}class Ja extends ii{}class xn extends Te{}class ki extends xn{}class fs extends xn{}class Bn extends Te{}class p0 extends Bn{}class h0 extends Bn{}class Hi extends Te{}class m0 extends Hi{}class C0 extends Hi{}class Or extends Te{}class b0 extends Or{}class I0 extends Or{}class It extends Te{}class Ht extends It{}class vn extends It{}class ss extends Te{}class w0 extends ss{}class Aa extends ss{}class ps extends Te{}class ta extends ps{}class hs extends ps{}class Ri extends Te{}class yn extends Ri{}class k0 extends Ri{}class Wi extends Te{}class vr extends Wi{}class xi extends Wi{}class En extends Te{}class ra extends En{}class ni extends En{}class ms extends Te{}class Mn extends ms{}class Gn extends ms{}class Dn extends Te{}class Vi extends Dn{}class is extends Dn{}class ji extends Te{}class x0 extends ji{}class Qn extends ji{}class Tn extends Te{}class Cs extends Tn{}class Ui extends Tn{}class Lr extends Te{}class Pn extends Lr{}class Ki extends Lr{}class Bi extends Te{}class za extends Bi{}class vi extends Bi{}class zn extends Te{}class _a extends zn{}class Fn extends zn{}class Zi extends Te{}class Sn extends Zi{}class oi extends Zi{}class Nn extends Te{}class B0 extends Nn{}class v0 extends Nn{}class aa extends Te{}class yi extends aa{}class ha extends aa{}class On extends Te{forward_params=["input_ids","attention_mask","position_ids","past_key_values","pixel_values","image_grid_thw"]}class Xi extends On{get_rope_index(U,Ee,eA,sA){const{vision_config:uA,image_token_id:FA,video_token_id:XA,vision_start_token_id:WA}=this.config,_A=uA.spatial_merge_size??2,ft=[];if(Ee||eA){let gt=U.tolist();sA||(sA=(0,H.ones_like)(U));const zt=sA.tolist(),Qt=Array.from({length:3},Mr=>Array.from({length:U.dims[0]},Vr=>Array.from({length:U.dims[1]},Jr=>1))),or=Ee?Ee.tolist():[],Bt=eA?eA.tolist():[];let Ot=0,Ir=0;for(let Mr=0;Mr<gt.length;++Mr){const Vr=gt[Mr].filter((Gr,oa)=>zt[Mr][oa]==1),Jr=Vr.reduce((Gr,oa,As)=>(oa==WA&&Gr.push(As),Gr),[]),Sa=Jr.map(Gr=>Vr[Gr+1]),_r=Sa.filter(Gr=>Gr==FA).length,Yr=Sa.filter(Gr=>Gr==XA).length;let Ca=[],_t=0,Ua=_r,rn=Yr;for(let Gr=0;Gr<Sa.length;++Gr){const oa=Vr.findIndex((Is,Ra)=>Ra>_t&&Is==FA),As=Vr.findIndex((Is,Ra)=>Ra>_t&&Is==XA),sn=Ua>0&&oa!==-1?oa:Vr.length+1,Pi=rn>0&&As!==-1?As:Vr.length+1;let N0,eo,Ao,to;sn<Pi?([eo,Ao,to]=or[Ot],++Ot,--Ua,N0=sn):([eo,Ao,to]=Bt[Ir],++Ir,--rn,N0=Pi);const[f1,ro,qs]=[Number(eo),Math.floor(Number(Ao)/_A),Math.floor(Number(to)/_A)],bs=N0-_t,Jn=Ca.length>0?(0,be.max)(Ca.at(-1))[0]+1:0;Ca.push(Array.from({length:3*bs},(Is,Ra)=>Jn+Ra%bs));const _n=bs+Jn,nn=f1*ro*qs,Xo=Array.from({length:nn},(Is,Ra)=>_n+Math.floor(Ra/(ro*qs))),p1=Array.from({length:nn},(Is,Ra)=>_n+Math.floor(Ra/qs)%ro),qo=Array.from({length:nn},(Is,Ra)=>_n+Ra%qs);Ca.push([Xo,p1,qo].flat()),_t=N0+nn}if(_t<Vr.length){const Gr=Ca.length>0?(0,be.max)(Ca.at(-1))[0]+1:0,oa=Vr.length-_t;Ca.push(Array.from({length:3*oa},(As,sn)=>Gr+sn%oa))}const $0=Ca.reduce((Gr,oa)=>Gr+oa.length,0),an=new Array($0);let qn=0;for(let Gr=0;Gr<3;++Gr)for(let oa=0;oa<Ca.length;++oa){const As=Ca[oa],sn=As.length/3;for(let Pi=Gr*sn;Pi<(Gr+1)*sn;++Pi)an[qn++]=As[Pi]}let u1=0;const d1=zt[Mr];for(let Gr=0;Gr<d1.length;++Gr)if(d1[Gr]==1){for(let oa=0;oa<3;++oa)Qt[oa][Mr][Gr]=an[oa*$0/3+u1];++u1}const Y1=(0,be.max)(an)[0];ft.push(Y1+1-gt[Mr].length)}return[new H.Tensor("int64",Qt.flat(1/0),[3,U.dims[0],U.dims[1]]),new H.Tensor("int64",ft,[ft.length,1])]}if(sA){const{data:gt,dims:zt}=nA(sA),Qt=BigInt64Array.from({length:3*gt.length},(Bt,Ot)=>gt[Ot%gt.length]),or=Array.from({length:zt[0]},(Bt,Ot)=>(0,be.max)(gt.subarray(zt[1]*Ot,zt[1]*(Ot+1)))[0]+1n+BigInt(zt[1]));return[new H.Tensor("int64",Qt,[3,...zt]),new H.Tensor("int64",or,[or.length,1])]}{const[gt,zt]=U.dims,Qt=BigInt64Array.from({length:3*gt*zt},(or,Bt)=>BigInt(Math.floor(Bt%zt/gt)));return[new H.Tensor("int64",Qt,[3,...U.dims]),(0,H.zeros)([gt,1])]}}async encode_image({pixel_values:U,image_grid_thw:Ee}){return(await Ge(this.sessions.vision_encoder,{pixel_values:U,grid_thw:Ee})).image_features}_merge_input_ids_with_image_features(U){return iA({image_token_id:this.config.image_token_id,...U})}prepare_inputs_for_generation(U,Ee,eA){if(Ee.attention_mask&&!Ee.position_ids)if(Ee.past_key_values){Ee.pixel_values=null;const sA=BigInt(Object.values(Ee.past_key_values)[0].dims.at(-2)),uA=Ee.rope_deltas.map(FA=>sA+FA);Ee.position_ids=(0,H.stack)([uA,uA,uA],0)}else[Ee.position_ids,Ee.rope_deltas]=this.get_rope_index(Ee.input_ids,Ee.image_grid_thw,Ee.video_grid_thw,Ee.attention_mask);return Ee}}class Va extends Te{}class fr extends Va{}class Ln extends Va{}class Ms extends Te{}class li extends Ms{}class y0 extends Ms{}class Yn extends Te{}class Hn extends Yn{}class E0 extends Yn{}class Gs extends Te{}class $a extends Gs{}class Rn extends Gs{}class Ei extends Te{}class Zr extends Ei{}class ar extends Ei{}class ci extends Te{}class M0 extends ci{}class Ds extends ci{async _call(U){return new Nt(await super._call(U))}}class Qs extends Te{}class Us extends Qs{}class Wn extends Qs{async _call(U){return new Nt(await super._call(U))}}class ma extends Te{}class G0 extends ma{}class Vn extends Te{}class gi extends Vn{}class Mi extends Vn{async _call(U){return new Nt(await super._call(U))}}class sa extends Te{}class jn extends sa{}class qi extends Te{}class Ks extends qi{}class D0 extends qi{async _call(U){return new Nt(await super._call(U))}}class ui extends Te{}class ct extends ui{}class A extends Te{}class t extends A{}class r extends A{async _call(U){return new Nt(await super._call(U))}}class a extends Te{}class s extends a{async _call(U){return new O1(await super._call(U))}}class i extends Te{}class o extends i{}class c extends i{async _call(U){return new Nt(await super._call(U))}}class f extends Te{}class m extends f{}class p extends f{async _call(U){return new Nt(await super._call(U))}}class I extends Te{}class E extends I{}class T extends I{}class L extends Te{}class Z extends L{}class ee extends L{}class ce extends Te{}class he extends ce{}class ve extends ce{async _call(U){return new Nt(await super._call(U))}}class Qe extends Te{}class We extends Qe{}class Ke extends Qe{async _call(U){return new cA(await super._call(U))}}class rA extends Qe{async _call(U){return new MA(await super._call(U))}}class cA extends pA{constructor({logits:U,pred_boxes:Ee}){super(),this.logits=U,this.pred_boxes=Ee}}class MA extends pA{constructor({logits:U,pred_boxes:Ee,pred_masks:eA}){super(),this.logits=U,this.pred_boxes=Ee,this.pred_masks=eA}}class zA extends Te{}class et extends zA{}class at extends zA{async _call(U){return new dt(await super._call(U))}}class dt extends pA{constructor({logits:U,pred_boxes:Ee}){super(),this.logits=U,this.pred_boxes=Ee}}class Ft extends Te{}class Zt extends Ft{}class er extends Ft{async _call(U){return new pr(await super._call(U))}}class pr extends cA{}class hr extends Te{}class yr extends hr{}class Er extends hr{async _call(U){return new Nt(await super._call(U))}}class Fr extends Te{}class Wr extends Fr{}class ia extends Fr{async _call(U){return new Nt(await super._call(U))}}class Xr extends Te{}class na extends Xr{}class Ba extends Xr{async _call(U){return new Nt(await super._call(U))}}class es extends Te{}class ns extends es{}class os extends es{async _call(U){return new Nt(await super._call(U))}}class Ts extends Te{}class Gi extends Ts{}class di extends Ts{}class fi extends Te{}class Di extends fi{}class Un extends fi{}class Q0 extends Te{}class T0 extends Q0{}class Ji extends Te{}class l2 extends Ji{}class c2 extends Ji{}class g2 extends Ji{}class u2 extends Te{}class d2 extends u2{}class go extends Te{}class f2 extends go{}class p2 extends go{}class uo extends Te{}class h2 extends uo{}class m2 extends uo{}class C2 extends Te{}class b2 extends C2{}class fo extends Te{}class I2 extends fo{}class w2 extends fo{async _call(U){return new Nt(await super._call(U))}}class po extends Te{}class k2 extends po{}class x2 extends po{async _call(U){return new Nt(await super._call(U))}}class ho extends Te{}class B2 extends ho{}class Zs extends ho{async _call(U){return new Nt(await super._call(U))}}class Xs extends Te{}class _i extends Xs{}class $i extends Xs{async _call(U){return new Nt(await super._call(U))}}class v2 extends Te{}class y2 extends v2{}class mo extends Te{}class E2 extends mo{}class M2 extends mo{async _call(U){return new G2(await super._call(U))}}class G2 extends pA{constructor({logits:U,pred_boxes:Ee}){super(),this.logits=U,this.pred_boxes=Ee}}class D2 extends Te{}class Q2 extends D2{async get_image_embeddings({pixel_values:U}){return await kA(this,{pixel_values:U})}async forward(U){if(U.image_embeddings&&U.image_positional_embeddings||(U={...U,...await this.get_image_embeddings(U)}),!U.input_labels&&U.input_points){const eA=U.input_points.dims.slice(0,-1),sA=eA.reduce((uA,FA)=>uA*FA,1);U.input_labels=new H.Tensor("int64",new BigInt64Array(sA).fill(1n),eA)}const Ee={image_embeddings:U.image_embeddings,image_positional_embeddings:U.image_positional_embeddings};return U.input_points&&(Ee.input_points=U.input_points),U.input_labels&&(Ee.input_labels=U.input_labels),U.input_boxes&&(Ee.input_boxes=U.input_boxes),await Ge(this.sessions.prompt_encoder_mask_decoder,Ee)}async _call(U){return new T2(await super._call(U))}}class T2 extends pA{constructor({iou_scores:U,pred_masks:Ee}){super(),this.iou_scores=U,this.pred_masks=Ee}}class Ha extends Te{}class P2 extends Ha{}class z2 extends Ha{}class U0 extends Te{}class F2 extends U0{}class P0 extends U0{}class Qi extends Te{}class S2 extends Qi{}class N2 extends Qi{async _call(U){return new Xn(await super._call(U))}}class O2 extends Qi{async _call(U){return new Nt(await super._call(U))}}class L2 extends Qi{async _call(U){return new Da(await super._call(U))}}class K0 extends Te{}class Y2 extends K0{}class H2 extends K0{async _call(U){return new Da(await super._call(U))}}class R2 extends Te{}class W2 extends R2{}class z0 extends Te{}class V2 extends z0{}class Co extends z0{async _call(U){return new Xn(await super._call(U))}}class j2 extends z0{async _call(U){return new Nt(await super._call(U))}}class F0 extends Te{}class U2 extends F0{}class K2 extends F0{async _call(U){return new Xn(await super._call(U))}}class Z2 extends F0{async _call(U){return new Nt(await super._call(U))}}class X2 extends F0{async _call(U){return new Da(await super._call(U))}}class Z0 extends Te{}class q2 extends Z0{}class J2 extends Z0{async _call(U){return new Xn(await super._call(U))}}class _2 extends Z0{async _call(U){return new Nt(await super._call(U))}}class B1 extends Te{}class $2 extends Qi{}class el extends Qi{async _call(U){return new Xn(await super._call(U))}}class Al extends Qi{async _call(U){return new Nt(await super._call(U))}}class Kn extends Te{}class tl extends Kn{}class rl extends Kn{async _call(U){return new Xn(await super._call(U))}}class al extends Kn{async _call(U){return new Nt(await super._call(U))}}class sl extends Kn{async _call(U){return new N1(await super._call(U))}}class il extends Kn{async _call(U){return new Da(await super._call(U))}}class nl extends Te{}class ol extends nl{}class en extends Te{}class ll extends en{}class bo extends en{}class Io extends en{async generate_speech(U,Ee,{threshold:eA=.5,minlenratio:sA=0,maxlenratio:uA=20,vocoder:FA=null}={}){const XA={input_ids:U},{encoder_outputs:WA,encoder_attention_mask:_A}=await kA(this,XA),ft=WA.dims[1]/this.config.reduction_factor,gt=Math.floor(ft*uA),zt=Math.floor(ft*sA),Qt=this.config.num_mel_bins;let or=[],Bt=null,Ot=null,Ir=0;for(;;){++Ir;const Jr=Ue(!!Ot);let Sa;Sa=Ot?Ot.output_sequence_out:new H.Tensor("float32",new Float32Array(Qt),[1,1,Qt]);let _r={use_cache_branch:Jr,output_sequence:Sa,encoder_attention_mask:_A,speaker_embeddings:Ee,encoder_hidden_states:WA};this.addPastKeyValues(_r,Bt),Ot=await Ge(this.sessions.decoder_model_merged,_r),Bt=this.getPastKeyValues(Ot,Bt);const{prob:Yr,spectrum:Ca}=Ot;if(or.push(Ca),Ir>=zt&&(Array.from(Yr.data).filter(_t=>_t>=eA).length>0||Ir>=gt))break}const Mr=(0,H.cat)(or),{waveform:Vr}=await Ge(FA.sessions.model,{spectrogram:Mr});return{spectrogram:Mr,waveform:Vr}}}class wo extends Te{main_input_name="spectrogram"}class cl extends Te{}class gl extends cl{}class X0 extends Te{}class ko extends X0{}class ul extends X0{}class xo extends Te{}class Bo extends xo{}class dl extends xo{}class vo extends Te{}class yo extends vo{}class fl extends vo{}class q0 extends Te{}class pl extends q0{}class hl extends q0{static async from_pretrained(U,Ee={}){return super.from_pretrained(U,{...Ee,model_file_name:Ee.model_file_name??"text_model"})}}class ml extends q0{static async from_pretrained(U,Ee={}){return super.from_pretrained(U,{...Ee,model_file_name:Ee.model_file_name??"audio_model"})}}class Cl extends Te{}class Eo extends Cl{async _call(U){return new L1(await super._call(U))}}class J0 extends Te{}class v1 extends J0{}class bl extends J0{}class Il extends J0{}class Mo extends Te{}class wl extends Mo{}class kl extends Mo{}class Go extends Te{}class xl extends Go{}class Bl extends Go{async _call(U){return new Nt(await super._call(U))}}class Do extends Te{}class vl extends Do{}class y1 extends Do{}class Qo extends Te{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];_apply_and_filter_by_delay_pattern_mask(U){const[Ee,eA]=U.dims,sA=this.config.decoder.num_codebooks,uA=eA-sA;let FA=0;for(let _A=0;_A<U.size;++_A){if(U.data[_A]===this.config.decoder.pad_token_id)continue;const ft=_A%eA-Math.floor(_A/eA)%sA;ft>0&&ft<=uA&&(U.data[FA++]=U.data[_A])}const XA=Math.floor(Ee/sA),WA=FA/(XA*sA);return new H.Tensor(U.type,U.data.slice(0,FA),[XA,sA,WA])}prepare_inputs_for_generation(U,Ee,eA){let sA=structuredClone(U);for(let uA=0;uA<sA.length;++uA)for(let FA=0;FA<sA[uA].length;++FA)uA%this.config.decoder.num_codebooks>=FA&&(sA[uA][FA]=BigInt(this.config.decoder.pad_token_id));return eA.guidance_scale!==null&&eA.guidance_scale>1&&(sA=sA.concat(sA)),super.prepare_inputs_for_generation(sA,Ee,eA)}async generate(U){const Ee=await super.generate(U),eA=this._apply_and_filter_by_delay_pattern_mask(Ee).unsqueeze_(0),{audio_values:sA}=await Ge(this.sessions.encodec_decode,{audio_codes:eA});return sA}}class To extends Te{}class yl extends To{}class El extends To{async _call(U){return new Nt(await super._call(U))}}class Po extends Te{}class Ml extends Po{}class Gl extends Po{async _call(U){return new Nt(await super._call(U))}}class _0 extends Te{}class Dl extends _0{}class Ql extends _0{async _call(U){return new Nt(await super._call(U))}}class zo extends Te{}class Tl extends zo{}class Pl extends zo{async _call(U){return new Nt(await super._call(U))}}class zl extends Te{}class Fl extends zl{}class Sl extends Te{}class Nl extends Sl{forward_params=["input_ids","pixel_values","images_seq_mask","images_emb_mask","attention_mask","position_ids","past_key_values"];constructor(...U){super(...U),this._generation_mode="text"}async forward(U){const Ee=this._generation_mode??"text";let eA;if(Ee!=="text"&&U.past_key_values){const WA=this.sessions.gen_img_embeds,_A=(0,V.pick)({image_ids:U.input_ids},WA.inputNames);eA=await Ge(WA,_A)}else{const WA=this.sessions.prepare_inputs_embeds,_A=(0,V.pick)(U,WA.inputNames);eA=await Ge(WA,_A)}const sA={...U,...eA},uA=await aA(this,sA),FA=this.sessions[Ee==="text"?"lm_head":"gen_head"];if(!FA)throw new Error(`Unable to find "${FA}" generation head`);const XA=await Ge(FA,(0,V.pick)(uA,FA.inputNames));return{...eA,...uA,...XA}}async generate(U){return this._generation_mode="text",super.generate(U)}async generate_images(U){this._generation_mode="image";const Ee=(U.inputs??U[this.main_input_name]).dims[1],eA=(await super.generate(U)).slice(null,[Ee,null]),sA=this.sessions.image_decode,{decoded_image:uA}=await Ge(sA,{generated_tokens:eA}),FA=uA.add_(1).mul_(127.5).clamp_(0,255).to("uint8"),XA=[];for(const WA of FA){const _A=re.RawImage.fromTensor(WA);XA.push(_A)}return XA}}class Ol extends pA{constructor({char_logits:U,bpe_logits:Ee,wp_logits:eA}){super(),this.char_logits=U,this.bpe_logits=Ee,this.wp_logits=eA}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class Ll extends Te{}class Yl extends Ll{async _call(U){return new Ol(await super._call(U))}}class Fo extends Te{}class Hl extends Fo{}class S0 extends Fo{}class So extends Te{}class No extends So{}class Rl extends So{}class br{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(U,{progress_callback:Ee=null,config:eA=null,cache_dir:sA=null,local_files_only:uA=!1,revision:FA="main",model_file_name:XA=null,subfolder:WA="onnx",device:_A=null,dtype:ft=null,use_external_data_format:gt=null,session_options:zt={}}={}){const Qt={progress_callback:Ee,config:eA,cache_dir:sA,local_files_only:uA,revision:FA,model_file_name:XA,subfolder:WA,device:_A,dtype:ft,use_external_data_format:gt,session_options:zt};if(Qt.config=await k.AutoConfig.from_pretrained(U,Qt),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(const or of this.MODEL_CLASS_MAPPINGS){const Bt=or.get(Qt.config.model_type);if(Bt)return await Bt[1].from_pretrained(U,Qt)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${Qt.config.model_type}", attempting to construct from base class.`),await Te.from_pretrained(U,Qt);throw Error(`Unsupported model type: ${Qt.config.model_type}`)}}const E1=new Map([["bert",["BertModel",YA]],["modernbert",["ModernBertModel",Se]],["nomic_bert",["NomicBertModel",QA]],["roformer",["RoFormerModel",Mt]],["electra",["ElectraModel",ZA]],["esm",["EsmModel",Ss]],["convbert",["ConvBertModel",tr]],["camembert",["CamembertModel",zs]],["deberta",["DebertaModel",Ea]],["deberta-v2",["DebertaV2Model",us]],["mpnet",["MPNetModel",Ai]],["albert",["AlbertModel",fA]],["distilbert",["DistilBertModel",ts]],["roberta",["RobertaModel",Fi]],["xlm",["XLMModel",s0]],["xlm-roberta",["XLMRobertaModel",l0]],["clap",["ClapModel",pl]],["clip",["CLIPModel",RA]],["clipseg",["CLIPSegModel",HA]],["chinese_clip",["ChineseCLIPModel",d0]],["siglip",["SiglipModel",wi]],["jina_clip",["JinaCLIPModel",si]],["mobilebert",["MobileBertModel",Ls]],["squeezebert",["SqueezeBertModel",_]],["wav2vec2",["Wav2Vec2Model",S2]],["wav2vec2-bert",["Wav2Vec2BertModel",q2]],["unispeech",["UniSpeechModel",V2]],["unispeech-sat",["UniSpeechSatModel",U2]],["hubert",["HubertModel",$2]],["wavlm",["WavLMModel",tl]],["audio-spectrogram-transformer",["ASTModel",dn]],["vits",["VitsModel",Eo]],["pyannote",["PyAnnoteModel",Y2]],["wespeaker-resnet",["WeSpeakerResNetModel",W2]],["detr",["DetrModel",We]],["rt_detr",["RTDetrModel",et]],["table-transformer",["TableTransformerModel",Zt]],["vit",["ViTModel",M0]],["ijepa",["IJepaModel",Us]],["pvt",["PvtModel",gi]],["vit_msn",["ViTMSNModel",Ks]],["vit_mae",["ViTMAEModel",jn]],["groupvit",["GroupViTModel",ct]],["fastvit",["FastViTModel",t]],["mobilevit",["MobileViTModel",o]],["mobilevitv2",["MobileViTV2Model",m]],["owlvit",["OwlViTModel",E]],["owlv2",["Owlv2Model",Z]],["beit",["BeitModel",he]],["deit",["DeiTModel",yr]],["hiera",["HieraModel",Wr]],["convnext",["ConvNextModel",I2]],["convnextv2",["ConvNextV2Model",k2]],["dinov2",["Dinov2Model",B2]],["dinov2_with_registers",["Dinov2WithRegistersModel",_i]],["resnet",["ResNetModel",na]],["swin",["SwinModel",ns]],["swin2sr",["Swin2SRModel",Gi]],["donut-swin",["DonutSwinModel",b2]],["yolos",["YolosModel",E2]],["dpt",["DPTModel",Di]],["glpn",["GLPNModel",h2]],["hifigan",["SpeechT5HifiGan",wo]],["efficientnet",["EfficientNetModel",xl]],["decision_transformer",["DecisionTransformerModel",Fl]],["patchtst",["PatchTSTForPrediction",Hl]],["patchtsmixer",["PatchTSMixerForPrediction",No]],["mobilenet_v1",["MobileNetV1Model",yl]],["mobilenet_v2",["MobileNetV2Model",Ml]],["mobilenet_v3",["MobileNetV3Model",Dl]],["mobilenet_v4",["MobileNetV4Model",Tl]],["maskformer",["MaskFormerModel",f2]],["mgp-str",["MgpstrForSceneTextRecognition",Yl]],["style_text_to_speech_2",["StyleTextToSpeech2Model",ol]]]),M1=new Map([["t5",["T5Model",Yt]],["longt5",["LongT5Model",sr]],["mt5",["MT5Model",KA]],["bart",["BartModel",fa]],["mbart",["MBartModel",gA]],["marian",["MarianModel",P2]],["whisper",["WhisperModel",c0]],["m2m_100",["M2M100Model",F2]],["blenderbot",["BlenderbotModel",Pr]],["blenderbot-small",["BlenderbotSmallModel",xr]]]),G1=new Map([["bloom",["BloomModel",Hn]],["jais",["JAISModel",p0]],["gpt2",["GPT2Model",ki]],["gptj",["GPTJModel",Ht]],["gpt_bigcode",["GPTBigCodeModel",w0]],["gpt_neo",["GPTNeoModel",m0]],["gpt_neox",["GPTNeoXModel",b0]],["codegen",["CodeGenModel",ta]],["llama",["LlamaModel",yn]],["exaone",["ExaoneModel",Mn]],["olmo",["OlmoModel",x0]],["olmo2",["Olmo2Model",Cs]],["mobilellm",["MobileLLMModel",Vi]],["granite",["GraniteModel",Pn]],["cohere",["CohereModel",za]],["gemma",["GemmaModel",_a]],["gemma2",["Gemma2Model",Sn]],["helium",["HeliumModel",vr]],["glm",["GlmModel",ra]],["openelm",["OpenELMModel",B0]],["qwen2",["Qwen2Model",yi]],["phi",["PhiModel",fr]],["phi3",["Phi3Model",li]],["mpt",["MptModel",$a]],["opt",["OPTModel",Zr]],["mistral",["MistralModel",ko]],["starcoder2",["Starcoder2Model",Bo]],["falcon",["FalconModel",yo]],["stablelm",["StableLmModel",wl]]]),Oo=new Map([["speecht5",["SpeechT5ForSpeechToText",bo]],["whisper",["WhisperForConditionalGeneration",g0]],["moonshine",["MoonshineForConditionalGeneration",Ta]]]),Wl=new Map([["speecht5",["SpeechT5ForTextToSpeech",Io]]]),Vl=new Map([["vits",["VitsModel",Eo]],["musicgen",["MusicgenForConditionalGeneration",Qo]]]),Lo=new Map([["bert",["BertForSequenceClassification",OA]],["modernbert",["ModernBertForSequenceClassification",dA]],["roformer",["RoFormerForSequenceClassification",ut]],["electra",["ElectraForSequenceClassification",PA]],["esm",["EsmForSequenceClassification",Ci]],["convbert",["ConvBertForSequenceClassification",bt]],["camembert",["CamembertForSequenceClassification",ks]],["deberta",["DebertaForSequenceClassification",Hr]],["deberta-v2",["DebertaV2ForSequenceClassification",_s]],["mpnet",["MPNetForSequenceClassification",Ys]],["albert",["AlbertForSequenceClassification",NA]],["distilbert",["DistilBertForSequenceClassification",ds]],["roberta",["RobertaForSequenceClassification",Si]],["xlm",["XLMForSequenceClassification",n0]],["xlm-roberta",["XLMRobertaForSequenceClassification",Qa]],["bart",["BartForSequenceClassification",rt]],["mbart",["MBartForSequenceClassification",pa]],["mobilebert",["MobileBertForSequenceClassification",wt]],["squeezebert",["SqueezeBertForSequenceClassification",Oe]]]),jl=new Map([["bert",["BertForTokenClassification",ge]],["modernbert",["ModernBertForTokenClassification",oA]],["roformer",["RoFormerForTokenClassification",Ct]],["electra",["ElectraForTokenClassification",qt]],["esm",["EsmForTokenClassification",Os]],["convbert",["ConvBertForTokenClassification",Ka]],["camembert",["CamembertForTokenClassification",Fs]],["deberta",["DebertaForTokenClassification",gs]],["deberta-v2",["DebertaV2ForTokenClassification",$s]],["mpnet",["MPNetForTokenClassification",rs]],["distilbert",["DistilBertForTokenClassification",ei]],["roberta",["RobertaForTokenClassification",Gt]],["xlm",["XLMForTokenClassification",o0]],["xlm-roberta",["XLMRobertaForTokenClassification",Kr]]]),Yo=new Map([["t5",["T5ForConditionalGeneration",Vt]],["longt5",["LongT5ForConditionalGeneration",jt]],["mt5",["MT5ForConditionalGeneration",Jt]],["bart",["BartForConditionalGeneration",ir]],["mbart",["MBartForConditionalGeneration",JA]],["marian",["MarianMTModel",z2]],["m2m_100",["M2M100ForConditionalGeneration",P0]],["blenderbot",["BlenderbotForConditionalGeneration",Cr]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",ti]]]),Ho=new Map([["bloom",["BloomForCausalLM",E0]],["gpt2",["GPT2LMHeadModel",fs]],["jais",["JAISLMHeadModel",h0]],["gptj",["GPTJForCausalLM",vn]],["gpt_bigcode",["GPTBigCodeForCausalLM",Aa]],["gpt_neo",["GPTNeoForCausalLM",C0]],["gpt_neox",["GPTNeoXForCausalLM",I0]],["codegen",["CodeGenForCausalLM",hs]],["llama",["LlamaForCausalLM",k0]],["exaone",["ExaoneForCausalLM",Gn]],["olmo",["OlmoForCausalLM",Qn]],["olmo2",["Olmo2ForCausalLM",Ui]],["mobilellm",["MobileLLMForCausalLM",is]],["granite",["GraniteForCausalLM",Ki]],["cohere",["CohereForCausalLM",vi]],["gemma",["GemmaForCausalLM",Fn]],["gemma2",["Gemma2ForCausalLM",oi]],["helium",["HeliumForCausalLM",xi]],["glm",["GlmForCausalLM",ni]],["openelm",["OpenELMForCausalLM",v0]],["qwen2",["Qwen2ForCausalLM",ha]],["phi",["PhiForCausalLM",Ln]],["phi3",["Phi3ForCausalLM",y0]],["mpt",["MptForCausalLM",Rn]],["opt",["OPTForCausalLM",ar]],["mbart",["MBartForCausalLM",Nr]],["mistral",["MistralForCausalLM",ul]],["starcoder2",["Starcoder2ForCausalLM",dl]],["falcon",["FalconForCausalLM",fl]],["trocr",["TrOCRForCausalLM",gl]],["stablelm",["StableLmForCausalLM",kl]],["phi3_v",["Phi3VForCausalLM",DA]]]),D1=new Map([["multi_modality",["MultiModalityCausalLM",Nl]]]),Ul=new Map([["bert",["BertForMaskedLM",J]],["modernbert",["ModernBertForMaskedLM",qe]],["roformer",["RoFormerForMaskedLM",Tt]],["electra",["ElectraForMaskedLM",$A]],["esm",["EsmForMaskedLM",Ns]],["convbert",["ConvBertForMaskedLM",da]],["camembert",["CamembertForMaskedLM",ws]],["deberta",["DebertaForMaskedLM",Tr]],["deberta-v2",["DebertaV2ForMaskedLM",hi]],["mpnet",["MPNetForMaskedLM",ys]],["albert",["AlbertForMaskedLM",VA]],["distilbert",["DistilBertForMaskedLM",xs]],["roberta",["RobertaForMaskedLM",Ma]],["xlm",["XLMWithLMHeadModel",i0]],["xlm-roberta",["XLMRobertaForMaskedLM",Ni]],["mobilebert",["MobileBertForMaskedLM",vs]],["squeezebert",["SqueezeBertForMaskedLM",Be]]]),Kl=new Map([["bert",["BertForQuestionAnswering",$]],["roformer",["RoFormerForQuestionAnswering",$t]],["electra",["ElectraForQuestionAnswering",cs]],["convbert",["ConvBertForQuestionAnswering",Oa]],["camembert",["CamembertForQuestionAnswering",$r]],["deberta",["DebertaForQuestionAnswering",Kt]],["deberta-v2",["DebertaV2ForQuestionAnswering",mi]],["mpnet",["MPNetForQuestionAnswering",Hs]],["albert",["AlbertForQuestionAnswering",He]],["distilbert",["DistilBertForQuestionAnswering",qa]],["roberta",["RobertaForQuestionAnswering",ea]],["xlm",["XLMForQuestionAnswering",ri]],["xlm-roberta",["XLMRobertaForQuestionAnswering",qA]],["mobilebert",["MobileBertForQuestionAnswering",Pt]],["squeezebert",["SqueezeBertForQuestionAnswering",Ve]]]),Ro=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",rr]],["idefics3",["Idefics3ForConditionalGeneration",bn]]]),Q1=new Map([["llava",["LlavaForConditionalGeneration",Ii]],["llava_onevision",["LlavaOnevisionForConditionalGeneration",ka]],["moondream1",["Moondream1ForConditionalGeneration",Ws]],["florence2",["Florence2ForConditionalGeneration",hn]],["qwen2-vl",["Qwen2VLForConditionalGeneration",Xi]],["idefics3",["Idefics3ForConditionalGeneration",bn]],["paligemma",["PaliGemmaForConditionalGeneration",Vs]]]),T1=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",rr]]]),Zl=new Map([["vit",["ViTForImageClassification",Ds]],["ijepa",["IJepaForImageClassification",Wn]],["pvt",["PvtForImageClassification",Mi]],["vit_msn",["ViTMSNForImageClassification",D0]],["fastvit",["FastViTForImageClassification",r]],["mobilevit",["MobileViTForImageClassification",c]],["mobilevitv2",["MobileViTV2ForImageClassification",p]],["beit",["BeitForImageClassification",ve]],["deit",["DeiTForImageClassification",Er]],["hiera",["HieraForImageClassification",ia]],["convnext",["ConvNextForImageClassification",w2]],["convnextv2",["ConvNextV2ForImageClassification",x2]],["dinov2",["Dinov2ForImageClassification",Zs]],["dinov2_with_registers",["Dinov2WithRegistersForImageClassification",$i]],["resnet",["ResNetForImageClassification",Ba]],["swin",["SwinForImageClassification",os]],["segformer",["SegformerForImageClassification",bl]],["efficientnet",["EfficientNetForImageClassification",Bl]],["mobilenet_v1",["MobileNetV1ForImageClassification",El]],["mobilenet_v2",["MobileNetV2ForImageClassification",Gl]],["mobilenet_v3",["MobileNetV3ForImageClassification",Ql]],["mobilenet_v4",["MobileNetV4ForImageClassification",Pl]]]),Xl=new Map([["detr",["DetrForObjectDetection",Ke]],["rt_detr",["RTDetrForObjectDetection",at]],["table-transformer",["TableTransformerForObjectDetection",er]],["yolos",["YolosForObjectDetection",M2]]]),ql=new Map([["owlvit",["OwlViTForObjectDetection",T]],["owlv2",["Owlv2ForObjectDetection",ee]],["grounding-dino",["GroundingDinoForObjectDetection",y2]]]),Jl=new Map([["detr",["DetrForSegmentation",rA]],["clipseg",["CLIPSegForImageSegmentation",Ja]]]),_l=new Map([["segformer",["SegformerForSemanticSegmentation",Il]],["sapiens",["SapiensForSemanticSegmentation",l2]]]),$l=new Map([["detr",["DetrForSegmentation",rA]],["maskformer",["MaskFormerForInstanceSegmentation",p2]]]),Wo=new Map([["sam",["SamModel",Q2]]]),e1=new Map([["wav2vec2",["Wav2Vec2ForCTC",N2]],["wav2vec2-bert",["Wav2Vec2BertForCTC",J2]],["unispeech",["UniSpeechForCTC",Co]],["unispeech-sat",["UniSpeechSatForCTC",K2]],["wavlm",["WavLMForCTC",rl]],["hubert",["HubertForCTC",el]]]),Vo=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",O2]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",_2]],["unispeech",["UniSpeechForSequenceClassification",j2]],["unispeech-sat",["UniSpeechSatForSequenceClassification",Z2]],["wavlm",["WavLMForSequenceClassification",al]],["hubert",["HubertForSequenceClassification",Al]],["audio-spectrogram-transformer",["ASTForAudioClassification",Oi]]]),jo=new Map([["wavlm",["WavLMForXVector",sl]]]),Uo=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",X2]],["wavlm",["WavLMForAudioFrameClassification",il]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",L2]],["pyannote",["PyAnnoteForAudioFrameClassification",H2]]]),A1=new Map([["vitmatte",["VitMatteForImageMatting",s]]]),P1=new Map([["patchtst",["PatchTSTForPrediction",S0]],["patchtsmixer",["PatchTSMixerForPrediction",Rl]]]),Ko=new Map([["swin2sr",["Swin2SRForImageSuperResolution",di]]]),t1=new Map([["dpt",["DPTForDepthEstimation",Un]],["depth_anything",["DepthAnythingForDepthEstimation",T0]],["glpn",["GLPNForDepthEstimation",m2]],["sapiens",["SapiensForDepthEstimation",c2]],["depth_pro",["DepthProForDepthEstimation",d2]]]),r1=new Map([["sapiens",["SapiensForNormalEstimation",g2]]]),z1=new Map([["vitpose",["VitPoseForPoseEstimation",G0]]]),Zo=new Map([["clip",["CLIPVisionModelWithProjection",Pa]],["siglip",["SiglipVisionModel",zr]],["jina_clip",["JinaCLIPVisionModel",Yi]]]),An=[[E1,g],[M1,b],[G1,M],[Lo,g],[jl,g],[Yo,e],[Oo,e],[Ho,M],[D1,Re],[Ul,g],[Kl,g],[Ro,l],[Q1,D],[Zl,g],[Jl,g],[$l,g],[_l,g],[A1,g],[P1,g],[Ko,g],[t1,g],[r1,g],[z1,g],[Xl,g],[ql,g],[Wo,ue],[e1,g],[Vo,g],[Wl,e],[Vl,g],[jo,g],[Uo,g],[Zo,g]];for(const[K,U]of An)for(const[Ee,eA]of K.values())Ye.set(Ee,U),AA.set(eA,Ee),Je.set(Ee,eA);const a1=[["MusicgenForConditionalGeneration",Qo,z],["Phi3VForCausalLM",DA,Ne],["CLIPTextModelWithProjection",Li,g],["SiglipTextModel",kn,g],["JinaCLIPTextModel",f0,g],["ClapTextModelWithProjection",hl,g],["ClapAudioModelWithProjection",ml,g]];for(const[K,U,Ee]of a1)Ye.set(K,Ee),AA.set(U,K),Je.set(K,U);class ic extends br{static MODEL_CLASS_MAPPINGS=An.map(U=>U[0]);static BASE_IF_FAIL=!0}class nc extends br{static MODEL_CLASS_MAPPINGS=[Lo]}class Dt extends br{static MODEL_CLASS_MAPPINGS=[jl]}class F1 extends br{static MODEL_CLASS_MAPPINGS=[Yo]}class Ti extends br{static MODEL_CLASS_MAPPINGS=[Oo]}class S1 extends br{static MODEL_CLASS_MAPPINGS=[Wl]}class s1 extends br{static MODEL_CLASS_MAPPINGS=[Vl]}class oc extends br{static MODEL_CLASS_MAPPINGS=[Ho]}class qr extends br{static MODEL_CLASS_MAPPINGS=[Ul]}class St extends br{static MODEL_CLASS_MAPPINGS=[Kl]}class Rt extends br{static MODEL_CLASS_MAPPINGS=[Ro]}class tn extends br{static MODEL_CLASS_MAPPINGS=[Zl]}class lc extends br{static MODEL_CLASS_MAPPINGS=[Jl]}class i1 extends br{static MODEL_CLASS_MAPPINGS=[_l]}class cc extends br{static MODEL_CLASS_MAPPINGS=[$l]}class n1 extends br{static MODEL_CLASS_MAPPINGS=[Xl]}class gc extends br{static MODEL_CLASS_MAPPINGS=[ql]}class uc extends br{static MODEL_CLASS_MAPPINGS=[Wo]}class o1 extends br{static MODEL_CLASS_MAPPINGS=[e1]}class dc extends br{static MODEL_CLASS_MAPPINGS=[Vo]}class fc extends br{static MODEL_CLASS_MAPPINGS=[jo]}class pc extends br{static MODEL_CLASS_MAPPINGS=[Uo]}class Zn extends br{static MODEL_CLASS_MAPPINGS=[T1]}class l1 extends br{static MODEL_CLASS_MAPPINGS=[A1]}class hc extends br{static MODEL_CLASS_MAPPINGS=[Ko]}class c1 extends br{static MODEL_CLASS_MAPPINGS=[t1]}class g1 extends br{static MODEL_CLASS_MAPPINGS=[r1]}class mc extends br{static MODEL_CLASS_MAPPINGS=[z1]}class Cc extends br{static MODEL_CLASS_MAPPINGS=[Zo]}class bc extends pA{constructor({logits:U,past_key_values:Ee,encoder_outputs:eA,decoder_attentions:sA=null,cross_attentions:uA=null}){super(),this.logits=U,this.past_key_values=Ee,this.encoder_outputs=eA,this.decoder_attentions=sA,this.cross_attentions=uA}}class Nt extends pA{constructor({logits:U,...Ee}){super(),this.logits=U;const eA=Object.values(Ee);eA.length>0&&(this.attentions=eA)}}class N1 extends pA{constructor({logits:U,embeddings:Ee}){super(),this.logits=U,this.embeddings=Ee}}class Da extends pA{constructor({logits:U}){super(),this.logits=U}}class Fa extends pA{constructor({logits:U}){super(),this.logits=U}}class ja extends pA{constructor({start_logits:U,end_logits:Ee}){super(),this.start_logits=U,this.end_logits=Ee}}class Xn extends pA{constructor({logits:U}){super(),this.logits=U}}class Ic extends pA{constructor({logits:U,past_key_values:Ee}){super(),this.logits=U,this.past_key_values=Ee}}class O1 extends pA{constructor({alphas:U}){super(),this.alphas=U}}class L1 extends pA{constructor({waveform:U,spectrogram:Ee}){super(),this.waveform=U,this.spectrogram=Ee}}},"./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js":(x,C,d)=>{d.r(C),d.d(C,{ASTFeatureExtractor:()=>j});var k=d("./src/base/feature_extraction_utils.js");d("./src/utils/tensor.js");var P=d("./src/utils/audio.js");class j extends k.FeatureExtractor{constructor(V){super(V);const q=this.config.sampling_rate,R=(0,P.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(q/2),q,null,"kaldi",!0);for(let S=0;S<R.length;++S)R[S].push(0);this.mel_filters=R,this.window=(0,P.window_function)(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(V,q){return(0,P.spectrogram)(V,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:q,transpose:!0})}async _call(V){(0,k.validate_audio_inputs)(V,"ASTFeatureExtractor");const q=await this._extract_fbank_features(V,this.config.max_length);if(this.config.do_normalize){const R=2*this.std,S=q.data;for(let X=0;X<S.length;++X)S[X]=(S[X]-this.mean)/R}return{input_values:q.unsqueeze_(0)}}}},"./src/models/auto/feature_extraction_auto.js":(x,C,d)=>{d.r(C),d.d(C,{AutoFeatureExtractor:()=>O});var k=d("./src/utils/constants.js"),P=d("./src/utils/hub.js");d("./src/base/feature_extraction_utils.js");var j=d("./src/models/feature_extractors.js");class O{static async from_pretrained(q,R={}){const S=await(0,P.getModelJSON)(q,k.FEATURE_EXTRACTOR_NAME,!0,R),X=S.feature_extractor_type,H=j[X];if(!H)throw new Error(`Unknown feature_extractor_type: '${X}'. Please report this at ${k.GITHUB_ISSUE_URL}.`);return new H(S)}}},"./src/models/auto/image_processing_auto.js":(x,C,d)=>{d.r(C),d.d(C,{AutoImageProcessor:()=>V});var k=d("./src/utils/constants.js"),P=d("./src/utils/hub.js"),j=d("./src/base/image_processors_utils.js"),O=d("./src/models/image_processors.js");class V{static async from_pretrained(R,S={}){const X=await(0,P.getModelJSON)(R,k.IMAGE_PROCESSOR_NAME,!0,S),H=X.image_processor_type??X.feature_extractor_type;let re=O[H];return re||(H!==void 0&&console.warn(`Image processor type '${H}' not found, assuming base ImageProcessor. Please report this at ${k.GITHUB_ISSUE_URL}.`),re=j.ImageProcessor),new re(X)}}},"./src/models/auto/processing_auto.js":(x,C,d)=>{d.r(C),d.d(C,{AutoProcessor:()=>R});var k=d("./src/utils/constants.js"),P=d("./src/utils/hub.js"),j=d("./src/base/processing_utils.js"),O=d("./src/models/processors.js"),V=d("./src/models/image_processors.js"),q=d("./src/models/feature_extractors.js");class R{static async from_pretrained(X,H={}){const re=await(0,P.getModelJSON)(X,k.IMAGE_PROCESSOR_NAME,!0,H),{image_processor_type:be,feature_extractor_type:xe,processor_class:ye}=re;if(ye&&O[ye])return O[ye].from_pretrained(X,H);if(!be&&!xe)throw new Error("No `image_processor_type` or `feature_extractor_type` found in the config.");const se={};if(be){const ne=V[be];if(!ne)throw new Error(`Unknown image_processor_type: '${be}'.`);se.image_processor=new ne(re)}if(xe){const ne=V[xe];if(ne)se.image_processor=new ne(re);else{const we=q[xe];if(!we)throw new Error(`Unknown feature_extractor_type: '${xe}'.`);se.feature_extractor=new we(re)}}return new j.Processor({},se)}}},"./src/models/beit/image_processing_beit.js":(x,C,d)=>{d.r(C),d.d(C,{BeitFeatureExtractor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{}},"./src/models/bit/image_processing_bit.js":(x,C,d)=>{d.r(C),d.d(C,{BitImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{}},"./src/models/chinese_clip/image_processing_chinese_clip.js":(x,C,d)=>{d.r(C),d.d(C,{ChineseCLIPFeatureExtractor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{}},"./src/models/clap/feature_extraction_clap.js":(x,C,d)=>{d.r(C),d.d(C,{ClapFeatureExtractor:()=>j});var k=d("./src/base/feature_extraction_utils.js");d("./src/utils/tensor.js");var P=d("./src/utils/audio.js");class j extends k.FeatureExtractor{constructor(V){super(V),this.mel_filters=(0,P.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=(0,P.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=(0,P.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(V,q,R,S){let X;const H=V.length-q;if(H>0){if(R!=="rand_trunc")throw new Error(`Truncation strategy "${R}" not implemented`);{const re=Math.floor(Math.random()*(H+1));V=V.subarray(re,re+q),X=await this._extract_fbank_features(V,this.mel_filters_slaney,this.config.nb_max_samples)}}else{if(H<0){let re=new Float64Array(q);if(re.set(V),S==="repeat")for(let be=V.length;be<q;be+=V.length)re.set(V.subarray(0,Math.min(V.length,q-be)),be);else if(S==="repeatpad")for(let be=V.length;be<-H;be+=V.length)re.set(V,be);V=re}if(R==="fusion")throw new Error(`Truncation strategy "${R}" not implemented`);X=await this._extract_fbank_features(V,this.mel_filters_slaney,this.config.nb_max_samples)}return X.unsqueeze_(0)}async _extract_fbank_features(V,q,R=null){return(0,P.spectrogram)(V,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:q,log_mel:"dB",max_num_frames:R,do_pad:!1,transpose:!0})}async _call(V,{max_length:q=null}={}){return(0,k.validate_audio_inputs)(V,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel(V,q??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}}},"./src/models/clip/image_processing_clip.js":(x,C,d)=>{d.r(C),d.d(C,{CLIPFeatureExtractor:()=>j,CLIPImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{}class j extends P{}},"./src/models/convnext/image_processing_convnext.js":(x,C,d)=>{d.r(C),d.d(C,{ConvNextFeatureExtractor:()=>j,ConvNextImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{constructor(V){super(V),this.crop_pct=this.config.crop_pct??.875}async resize(V){const q=this.size?.shortest_edge;if(q===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(q<384){const R=Math.floor(q/this.crop_pct),[S,X]=this.get_resize_output_image_size(V,{shortest_edge:R});V=await V.resize(S,X,{resample:this.resample}),V=await V.center_crop(q,q)}else V=await V.resize(q,q,{resample:this.resample});return V}}class j extends P{}},"./src/models/deit/image_processing_deit.js":(x,C,d)=>{d.r(C),d.d(C,{DeiTFeatureExtractor:()=>j,DeiTImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{}class j extends P{}},"./src/models/detr/image_processing_detr.js":(x,C,d)=>{d.r(C),d.d(C,{DetrFeatureExtractor:()=>O,DetrImageProcessor:()=>j});var k=d("./src/base/image_processors_utils.js"),P=d("./src/utils/tensor.js");class j extends k.ImageProcessor{async _call(q){const R=await super._call(q),S=[R.pixel_values.dims[0],64,64],X=(0,P.full)(S,1n);return{...R,pixel_mask:X}}post_process_object_detection(...q){return(0,k.post_process_object_detection)(...q)}post_process_panoptic_segmentation(...q){return(0,k.post_process_panoptic_segmentation)(...q)}post_process_instance_segmentation(...q){return(0,k.post_process_instance_segmentation)(...q)}}class O extends j{}},"./src/models/donut/image_processing_donut.js":(x,C,d)=>{d.r(C),d.d(C,{DonutFeatureExtractor:()=>j,DonutImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{pad_image(V,q,R,S={}){const[X,H,re]=q;let be=this.image_mean;Array.isArray(this.image_mean)||(be=new Array(re).fill(be));let xe=this.image_std;Array.isArray(xe)||(xe=new Array(re).fill(be));const ye=be.map((se,ne)=>-se/xe[ne]);return super.pad_image(V,q,R,{center:!0,constant_values:ye,...S})}}class j extends P{}},"./src/models/dpt/image_processing_dpt.js":(x,C,d)=>{d.r(C),d.d(C,{DPTFeatureExtractor:()=>j,DPTImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{}class j extends P{}},"./src/models/efficientnet/image_processing_efficientnet.js":(x,C,d)=>{d.r(C),d.d(C,{EfficientNetImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{constructor(O){super(O),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(V=>V*V))}}},"./src/models/feature_extractors.js":(x,C,d)=>{d.r(C),d.d(C,{ASTFeatureExtractor:()=>k.ASTFeatureExtractor,ClapFeatureExtractor:()=>P.ClapFeatureExtractor,ImageFeatureExtractor:()=>H.ImageProcessor,MoonshineFeatureExtractor:()=>j.MoonshineFeatureExtractor,PyAnnoteFeatureExtractor:()=>O.PyAnnoteFeatureExtractor,SeamlessM4TFeatureExtractor:()=>V.SeamlessM4TFeatureExtractor,SpeechT5FeatureExtractor:()=>q.SpeechT5FeatureExtractor,Wav2Vec2FeatureExtractor:()=>R.Wav2Vec2FeatureExtractor,WeSpeakerFeatureExtractor:()=>S.WeSpeakerFeatureExtractor,WhisperFeatureExtractor:()=>X.WhisperFeatureExtractor});var k=d("./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js"),P=d("./src/models/clap/feature_extraction_clap.js"),j=d("./src/models/moonshine/feature_extraction_moonshine.js"),O=d("./src/models/pyannote/feature_extraction_pyannote.js"),V=d("./src/models/seamless_m4t/feature_extraction_seamless_m4t.js"),q=d("./src/models/speecht5/feature_extraction_speecht5.js"),R=d("./src/models/wav2vec2/feature_extraction_wav2vec2.js"),S=d("./src/models/wespeaker/feature_extraction_wespeaker.js"),X=d("./src/models/whisper/feature_extraction_whisper.js"),H=d("./src/base/image_processors_utils.js")},"./src/models/florence2/processing_florence2.js":(x,C,d)=>{d.r(C),d.d(C,{Florence2Processor:()=>O});var k=d("./src/base/processing_utils.js"),P=d("./src/models/auto/image_processing_auto.js"),j=d("./src/tokenizers.js");class O extends k.Processor{static tokenizer_class=j.AutoTokenizer;static image_processor_class=P.AutoImageProcessor;constructor(q,R){super(q,R);const{tasks_answer_post_processing_type:S,task_prompts_without_inputs:X,task_prompts_with_input:H}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(S??{})),this.task_prompts_without_inputs=new Map(Object.entries(X??{})),this.task_prompts_with_input=new Map(Object.entries(H??{})),this.regexes={quad_boxes:/(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm,bboxes:/([^<]+)?<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm},this.size_per_bin=1e3}construct_prompts(q){typeof q=="string"&&(q=[q]);const R=[];for(const S of q)if(this.task_prompts_without_inputs.has(S))R.push(this.task_prompts_without_inputs.get(S));else{for(const[X,H]of this.task_prompts_with_input)if(S.includes(X)){R.push(H.replaceAll("{input}",S).replaceAll(X,""));break}R.length!==q.length&&R.push(S)}return R}post_process_generation(q,R,S){const X=this.tasks_answer_post_processing_type.get(R)??"pure_text";let H;switch(q=q.replaceAll("<s>","").replaceAll("</s>",""),X){case"pure_text":H=q;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const re=X==="ocr"?"quad_boxes":"bboxes",be=q.matchAll(this.regexes[re]),xe=[],ye=[];for(const[se,ne,...we]of be)xe.push(ne?ne.trim():xe.at(-1)??""),ye.push(we.map((g,b)=>(Number(g)+.5)/this.size_per_bin*S[b%2]));H={labels:xe,[re]:ye};break;default:throw new Error(`Task "${R}" (of type "${X}") not yet implemented.`)}return{[R]:H}}async _call(q,R=null,S={}){if(!q&&!R)throw new Error("Either text or images must be provided");return{...await this.image_processor(q,S),...R?this.tokenizer(R,S):{}}}}},"./src/models/glpn/image_processing_glpn.js":(x,C,d)=>{d.r(C),d.d(C,{GLPNFeatureExtractor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{}},"./src/models/grounding_dino/image_processing_grounding_dino.js":(x,C,d)=>{d.r(C),d.d(C,{GroundingDinoImageProcessor:()=>j});var k=d("./src/base/image_processors_utils.js"),P=d("./src/utils/tensor.js");class j extends k.ImageProcessor{async _call(V){const q=await super._call(V),R=q.pixel_values.dims,S=(0,P.ones)([R[0],R[2],R[3]]);return{...q,pixel_mask:S}}}},"./src/models/grounding_dino/processing_grounding_dino.js":(x,C,d)=>{d.r(C),d.d(C,{GroundingDinoProcessor:()=>q});var k=d("./src/base/processing_utils.js"),P=d("./src/models/auto/image_processing_auto.js"),j=d("./src/tokenizers.js"),O=d("./src/base/image_processors_utils.js");function V(R,S){const X=R.dims.at(-1)-1,H=R.tolist();H.fill(!1,0,1),H.fill(!1,X);const re=S.tolist();return H.map((be,xe)=>be?xe:null).filter(be=>be!==null).map(be=>re[be])}class q extends k.Processor{static tokenizer_class=j.AutoTokenizer;static image_processor_class=P.AutoImageProcessor;async _call(S,X,H={}){const re=S?await this.image_processor(S,H):{};return{...X?this.tokenizer(X,H):{},...re}}post_process_grounded_object_detection(S,X,{box_threshold:H=.25,text_threshold:re=.25,target_sizes:be=null}={}){const{logits:xe,pred_boxes:ye}=S,se=xe.dims[0];if(be!==null&&be.length!==se)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const ne=xe.dims.at(1),we=xe.sigmoid(),g=we.max(-1).tolist(),b=ye.tolist().map(l=>l.map(M=>(0,O.center_to_corners_format)(M))),e=[];for(let l=0;l<se;++l){const M=be!==null?be[l]:null;M!==null&&(b[l]=b[l].map(Ne=>Ne.map((Ye,Je)=>Ye*M[(Je+1)%2])));const ue=g[l],D=[],z=[],Re=[];for(let Ne=0;Ne<ne;++Ne){const Ye=ue[Ne];if(Ye<=H)continue;const Je=b[l][Ne],AA=we[l][Ne];D.push(Ye),Re.push(Je);const xA=V(AA.gt(re),X[l]);z.push(xA)}e.push({scores:D,boxes:Re,labels:this.batch_decode(z)})}return e}}},"./src/models/idefics3/image_processing_idefics3.js":(x,C,d)=>{d.r(C),d.d(C,{Idefics3ImageProcessor:()=>j});var k=d("./src/base/image_processors_utils.js"),P=d("./src/utils/tensor.js");class j extends k.ImageProcessor{constructor(V){super(V),this.do_image_splitting=V.do_image_splitting??!0,this.max_image_size=V.max_image_size}get_resize_for_vision_encoder(V,q){let[R,S]=V.dims.slice(-2);const X=S/R;return S>=R?(S=Math.ceil(S/q)*q,R=Math.floor(S/X),R=Math.ceil(R/q)*q):(R=Math.ceil(R/q)*q,S=Math.floor(R*X),S=Math.ceil(S/q)*q),{height:R,width:S}}async _call(V,{do_image_splitting:q=null,return_row_col_info:R=!1}={}){let S;if(Array.isArray(V)){if(V.length===0||!V[0])throw new Error("No images provided.");S=Array.isArray(V[0])?V:[V]}else S=[[V]];let X=[],H=[],re=[];const be=[],xe=[];for(const l of S){let M=await Promise.all(l.map(z=>this.preprocess(z)));be.push(...M.map(z=>z.original_size)),xe.push(...M.map(z=>z.reshaped_input_size)),M.forEach(z=>z.pixel_values.unsqueeze_(0));const{longest_edge:ue}=this.max_image_size;let D;if(q??this.do_image_splitting){let z=new Array(M.length),Re=new Array(M.length);D=await Promise.all(M.map(async(Ne,Ye)=>{const Je=this.get_resize_for_vision_encoder(Ne.pixel_values,ue),AA=await(0,P.interpolate_4d)(Ne.pixel_values,{size:[Je.height,Je.width]}),{frames:xA,num_splits_h:GA,num_splits_w:Ge}=await this.split_image(AA,this.max_image_size);return z[Ye]=GA,Re[Ye]=Ge,(0,P.cat)(xA,0)})),H.push(z),re.push(Re)}else{const z=[ue,ue];D=await Promise.all(M.map(Re=>(0,P.interpolate_4d)(Re.pixel_values,{size:z}))),H.push(new Array(M.length).fill(0)),re.push(new Array(M.length).fill(0))}X.push((0,P.cat)(D,0))}const ye=X.length,[se,ne,we,g]=X[0].dims;let b,e;if(ye===1)b=X[0].unsqueeze_(0),e=(0,P.full)([ye,se,we,g],!0);else{const l=Math.max(...X.map(D=>D.dims.at(0)));e=(0,P.full)([ye,l,we,g],!0);const M=e.data,ue=l*we*g;for(let D=0;D<ye;++D){const z=X[D].dims[0];if(z<l){X[D]=(0,P.cat)([X[D],(0,P.full)([l-z,ne,we,g],0)],0);const Re=D*ue+z*we*g,Ne=(D+1)*ue;M.fill(!1,Re,Ne)}}b=(0,P.stack)(X,0)}return{pixel_values:b,pixel_attention_mask:e,original_sizes:be,reshaped_input_sizes:xe,...R?{rows:H,cols:re}:{}}}async split_image(V,{longest_edge:q}){const R=q,S=q,X=[],[H,re]=V.dims.slice(-2);let be=0,xe=0;if(H>R||re>S){be=Math.ceil(H/R),xe=Math.ceil(re/S);const ye=Math.ceil(H/be),se=Math.ceil(re/xe);for(let g=0;g<be;++g)for(let b=0;b<xe;++b){let e,l,M,ue;g===be-1?(l=H-ye,ue=H):(l=g*ye,ue=(g+1)*ye),b===xe-1?(e=re-se,M=re):(e=b*se,M=(b+1)*se);const D=[l,e],z=[ue,M],Re=await(0,P.slice)(V,D,z,[2,3]);X.push(Re)}const ne=R,we=S;H===ne&&re===we||(V=await(0,P.interpolate_4d)(V,{size:[ne,we]}))}return X.push(V),{frames:X,num_splits_h:be,num_splits_w:xe}}}},"./src/models/idefics3/processing_idefics3.js":(x,C,d)=>{d.r(C),d.d(C,{Idefics3Processor:()=>q});var k=d("./src/base/processing_utils.js"),P=d("./src/models/auto/image_processing_auto.js"),j=d("./src/tokenizers.js");d("./src/utils/image.js");var O=d("./src/utils/core.js");function V(R,S,X,H,re,be){return R===0&&S===0?function(xe,ye,se,ne){return`${ye}${ne}`+se.repeat(xe)+`${ye}`}(X,H,re,be):function(xe,ye,se,ne,we,g){let b="";for(let e=0;e<ye;++e){for(let l=0;l<se;++l)b+=ne+`<row_${e+1}_col_${l+1}>`+we.repeat(xe);b+=`
`}return b+=`
${ne}${g}`+we.repeat(xe)+`${ne}`,b}(X,R,S,H,re,be)}class q extends k.Processor{static image_processor_class=P.AutoImageProcessor;static tokenizer_class=j.AutoTokenizer;static uses_processor_config=!0;fake_image_token="<fake_token_around_image>";image_token="<image>";global_img_token="<global-img>";async _call(S,X=null,H={}){let re;H.return_row_col_info??=!0,X&&(re=await this.image_processor(X,H)),Array.isArray(S)||(S=[S]);const be=re.rows??[new Array(S.length).fill(0)],xe=re.cols??[new Array(S.length).fill(0)],ye=this.config.image_seq_len,se=[],ne=[];for(let we=0;we<S.length;++we){const g=S[we],b=be[we],e=xe[we];se.push((0,O.count)(g,this.image_token));const l=b.map((D,z)=>V(D,e[z],ye,this.fake_image_token,this.image_token,this.global_img_token)),M=g.split(this.image_token);if(M.length===0)throw new Error("The image token should be present in the text.");let ue=M[0];for(let D=0;D<l.length;++D)ue+=l[D]+M[D+1];ne.push(ue)}return{...this.tokenizer(ne),...re}}}},"./src/models/image_processors.js":(x,C,d)=>{d.r(C),d.d(C,{BeitFeatureExtractor:()=>k.BeitFeatureExtractor,BitImageProcessor:()=>P.BitImageProcessor,CLIPFeatureExtractor:()=>O.CLIPFeatureExtractor,CLIPImageProcessor:()=>O.CLIPImageProcessor,ChineseCLIPFeatureExtractor:()=>j.ChineseCLIPFeatureExtractor,ConvNextFeatureExtractor:()=>V.ConvNextFeatureExtractor,ConvNextImageProcessor:()=>V.ConvNextImageProcessor,DPTFeatureExtractor:()=>X.DPTFeatureExtractor,DPTImageProcessor:()=>X.DPTImageProcessor,DeiTFeatureExtractor:()=>q.DeiTFeatureExtractor,DeiTImageProcessor:()=>q.DeiTImageProcessor,DetrFeatureExtractor:()=>R.DetrFeatureExtractor,DetrImageProcessor:()=>R.DetrImageProcessor,DonutFeatureExtractor:()=>S.DonutFeatureExtractor,DonutImageProcessor:()=>S.DonutImageProcessor,EfficientNetImageProcessor:()=>H.EfficientNetImageProcessor,GLPNFeatureExtractor:()=>re.GLPNFeatureExtractor,GroundingDinoImageProcessor:()=>be.GroundingDinoImageProcessor,Idefics3ImageProcessor:()=>xe.Idefics3ImageProcessor,JinaCLIPImageProcessor:()=>se.JinaCLIPImageProcessor,LlavaOnevisionImageProcessor:()=>ne.LlavaOnevisionImageProcessor,Mask2FormerImageProcessor:()=>we.Mask2FormerImageProcessor,MaskFormerFeatureExtractor:()=>g.MaskFormerFeatureExtractor,MaskFormerImageProcessor:()=>g.MaskFormerImageProcessor,MobileNetV1FeatureExtractor:()=>b.MobileNetV1FeatureExtractor,MobileNetV1ImageProcessor:()=>b.MobileNetV1ImageProcessor,MobileNetV2FeatureExtractor:()=>e.MobileNetV2FeatureExtractor,MobileNetV2ImageProcessor:()=>e.MobileNetV2ImageProcessor,MobileNetV3FeatureExtractor:()=>l.MobileNetV3FeatureExtractor,MobileNetV3ImageProcessor:()=>l.MobileNetV3ImageProcessor,MobileNetV4FeatureExtractor:()=>M.MobileNetV4FeatureExtractor,MobileNetV4ImageProcessor:()=>M.MobileNetV4ImageProcessor,MobileViTFeatureExtractor:()=>ue.MobileViTFeatureExtractor,MobileViTImageProcessor:()=>ue.MobileViTImageProcessor,NougatImageProcessor:()=>D.NougatImageProcessor,OwlViTFeatureExtractor:()=>Re.OwlViTFeatureExtractor,OwlViTImageProcessor:()=>Re.OwlViTImageProcessor,Owlv2ImageProcessor:()=>z.Owlv2ImageProcessor,Phi3VImageProcessor:()=>Ne.Phi3VImageProcessor,PvtImageProcessor:()=>Ye.PvtImageProcessor,Qwen2VLImageProcessor:()=>Je.Qwen2VLImageProcessor,RTDetrImageProcessor:()=>AA.RTDetrImageProcessor,SamImageProcessor:()=>xA.SamImageProcessor,SegformerFeatureExtractor:()=>GA.SegformerFeatureExtractor,SegformerImageProcessor:()=>GA.SegformerImageProcessor,SiglipImageProcessor:()=>Ge.SiglipImageProcessor,Swin2SRImageProcessor:()=>Ie.Swin2SRImageProcessor,VLMImageProcessor:()=>ye.VLMImageProcessor,ViTFeatureExtractor:()=>ze.ViTFeatureExtractor,ViTImageProcessor:()=>ze.ViTImageProcessor,VitMatteImageProcessor:()=>Ue.VitMatteImageProcessor,VitPoseImageProcessor:()=>Pe.VitPoseImageProcessor,YolosFeatureExtractor:()=>kA.YolosFeatureExtractor,YolosImageProcessor:()=>kA.YolosImageProcessor});var k=d("./src/models/beit/image_processing_beit.js"),P=d("./src/models/bit/image_processing_bit.js"),j=d("./src/models/chinese_clip/image_processing_chinese_clip.js"),O=d("./src/models/clip/image_processing_clip.js"),V=d("./src/models/convnext/image_processing_convnext.js"),q=d("./src/models/deit/image_processing_deit.js"),R=d("./src/models/detr/image_processing_detr.js"),S=d("./src/models/donut/image_processing_donut.js"),X=d("./src/models/dpt/image_processing_dpt.js"),H=d("./src/models/efficientnet/image_processing_efficientnet.js"),re=d("./src/models/glpn/image_processing_glpn.js"),be=d("./src/models/grounding_dino/image_processing_grounding_dino.js"),xe=d("./src/models/idefics3/image_processing_idefics3.js"),ye=d("./src/models/janus/image_processing_janus.js"),se=d("./src/models/jina_clip/image_processing_jina_clip.js"),ne=d("./src/models/llava_onevision/image_processing_llava_onevision.js"),we=d("./src/models/mask2former/image_processing_mask2former.js"),g=d("./src/models/maskformer/image_processing_maskformer.js"),b=d("./src/models/mobilenet_v1/image_processing_mobilenet_v1.js"),e=d("./src/models/mobilenet_v2/image_processing_mobilenet_v2.js"),l=d("./src/models/mobilenet_v3/image_processing_mobilenet_v3.js"),M=d("./src/models/mobilenet_v4/image_processing_mobilenet_v4.js"),ue=d("./src/models/mobilevit/image_processing_mobilevit.js"),D=d("./src/models/nougat/image_processing_nougat.js"),z=d("./src/models/owlv2/image_processing_owlv2.js"),Re=d("./src/models/owlvit/image_processing_owlvit.js"),Ne=d("./src/models/phi3_v/image_processing_phi3_v.js"),Ye=d("./src/models/pvt/image_processing_pvt.js"),Je=d("./src/models/qwen2_vl/image_processing_qwen2_vl.js"),AA=d("./src/models/rt_detr/image_processing_rt_detr.js"),xA=d("./src/models/sam/image_processing_sam.js"),GA=d("./src/models/segformer/image_processing_segformer.js"),Ge=d("./src/models/siglip/image_processing_siglip.js"),Ie=d("./src/models/swin2sr/image_processing_swin2sr.js"),ze=d("./src/models/vit/image_processing_vit.js"),Ue=d("./src/models/vitmatte/image_processing_vitmatte.js"),Pe=d("./src/models/vitpose/image_processing_vitpose.js"),kA=d("./src/models/yolos/image_processing_yolos.js")},"./src/models/janus/image_processing_janus.js":(x,C,d)=>{d.r(C),d.d(C,{VLMImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{constructor(O){super({do_pad:!0,pad_size:{width:O.image_size,height:O.image_size},...O}),this.constant_values=this.config.background_color.map(V=>V*this.rescale_factor)}pad_image(O,V,q,R){return super.pad_image(O,V,q,{constant_values:this.constant_values,center:!0,...R})}}},"./src/models/janus/processing_janus.js":(x,C,d)=>{d.r(C),d.d(C,{VLChatProcessor:()=>R});var k=d("./src/base/processing_utils.js"),P=d("./src/models/auto/image_processing_auto.js"),j=d("./src/tokenizers.js"),O=d("./src/utils/core.js"),V=d("./src/utils/tensor.js"),q=d("./src/utils/image.js");class R extends k.Processor{static image_processor_class=P.AutoImageProcessor;static tokenizer_class=j.AutoTokenizer;static uses_processor_config=!0;constructor(X,H){super(X,H),this.image_tag=this.config.image_tag,this.image_start_tag=this.config.image_start_tag,this.image_end_tag=this.config.image_end_tag,this.num_image_tokens=this.config.num_image_tokens}async _call(X,{images:H=null,chat_template:re="default"}={}){H?Array.isArray(H)||(H=[H]):H=await Promise.all(X.filter(ue=>ue.images).flatMap(ue=>ue.images).map(ue=>q.RawImage.read(ue)));const be=this.tokenizer,xe=ue=>be.encode(ue,{add_special_tokens:!1}),ye=be.apply_chat_template(X,{tokenize:!1,add_generation_prompt:!0,chat_template:re}).split(this.image_tag),se=ye.length-1;if(H.length!==se)throw new Error(`Number of images provided (${H.length}) does not match number of "${this.image_tag}" image tags (${se})`);const[ne,we,g]=be.model.convert_tokens_to_ids([this.image_tag,this.image_start_tag,this.image_end_tag]);let b=xe(ye[0]),e=new Array(b.length).fill(!1);for(let ue=1;ue<ye.length;++ue){const D=new Array(this.num_image_tokens).fill(ne),z=xe(ye[ue]);b=(0,O.mergeArrays)(b,[we],D,[g],z);const Re=new Array(this.num_image_tokens).fill(!0);e=(0,O.mergeArrays)(e,[!1],Re,[!1],new Array(z.length).fill(!1))}const l=[1,b.length],M={input_ids:new V.Tensor("int64",b,l),attention_mask:new V.Tensor("int64",new Array(b.length).fill(1),l),images_seq_mask:new V.Tensor("bool",e,l),images_emb_mask:new V.Tensor("bool",new Array(se*this.num_image_tokens).fill(!0),[1,se,this.num_image_tokens])};if(H&&H.length>0){const ue=await this.image_processor(H);return ue.pixel_values.unsqueeze_(0),{...M,...ue}}return M}}},"./src/models/jina_clip/image_processing_jina_clip.js":(x,C,d)=>{d.r(C),d.d(C,{JinaCLIPImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{constructor(O){const{resize_mode:V,fill_color:q,interpolation:R,size:S,...X}=O;super({...X,size:V==="squash"?{width:S,height:S}:V==="shortest"?{shortest_edge:S}:{longest_edge:S},resample:R==="bicubic"?3:2,do_center_crop:!0,crop_size:S,do_normalize:!0})}}},"./src/models/jina_clip/processing_jina_clip.js":(x,C,d)=>{d.r(C),d.d(C,{JinaCLIPProcessor:()=>O});var k=d("./src/base/processing_utils.js"),P=d("./src/models/auto/image_processing_auto.js"),j=d("./src/tokenizers.js");class O extends k.Processor{static tokenizer_class=j.AutoTokenizer;static image_processor_class=P.AutoImageProcessor;async _call(q=null,R=null,S={}){if(!q&&!R)throw new Error("Either text or images must be provided");return{...q?this.tokenizer(q,S):{},...R?await this.image_processor(R,S):{}}}}},"./src/models/llava_onevision/image_processing_llava_onevision.js":(x,C,d)=>{d.r(C),d.d(C,{LlavaOnevisionImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{}},"./src/models/mask2former/image_processing_mask2former.js":(x,C,d)=>{d.r(C),d.d(C,{Mask2FormerImageProcessor:()=>P});var k=d("./src/models/maskformer/image_processing_maskformer.js");class P extends k.MaskFormerImageProcessor{}},"./src/models/maskformer/image_processing_maskformer.js":(x,C,d)=>{d.r(C),d.d(C,{MaskFormerFeatureExtractor:()=>j,MaskFormerImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{post_process_panoptic_segmentation(...V){return(0,k.post_process_panoptic_segmentation)(...V)}post_process_instance_segmentation(...V){return(0,k.post_process_instance_segmentation)(...V)}}class j extends P{}},"./src/models/mgp_str/processing_mgp_str.js":(x,C,d)=>{d.r(C),d.d(C,{MgpstrProcessor:()=>q});var k=d("./src/base/processing_utils.js"),P=d("./src/models/auto/image_processing_auto.js"),j=d("./src/tokenizers.js"),O=d("./src/utils/maths.js");const V={char:["char_decode",1],bpe:["bpe_decode",2],wp:["wp_decode",102]};class q extends k.Processor{static tokenizer_class=j.AutoTokenizer;static image_processor_class=P.AutoImageProcessor;get char_tokenizer(){return this.components.char_tokenizer}get bpe_tokenizer(){return this.components.bpe_tokenizer}get wp_tokenizer(){return this.components.wp_tokenizer}_decode_helper(S,X){if(!V.hasOwnProperty(X))throw new Error(`Format ${X} is not supported.`);const[H,re]=V[X],be=this[H].bind(this),[xe,ye]=S.dims,se=[],ne=[],we=S.tolist();for(let g=0;g<xe;++g){const b=we[g],e=[],l=[];for(let ue=1;ue<ye;++ue){const[D,z]=(0,O.max)((0,O.softmax)(b[ue]));if(l.push(D),z==re)break;e.push(z)}const M=l.length>0?l.reduce((ue,D)=>ue*D,1):0;ne.push(e),se.push(M)}return[be(ne),se]}char_decode(S){return this.char_tokenizer.batch_decode(S).map(X=>X.replaceAll(" ",""))}bpe_decode(S){return this.bpe_tokenizer.batch_decode(S)}wp_decode(S){return this.wp_tokenizer.batch_decode(S).map(X=>X.replaceAll(" ",""))}batch_decode([S,X,H]){const[re,be]=this._decode_helper(S,"char"),[xe,ye]=this._decode_helper(X,"bpe"),[se,ne]=this._decode_helper(H,"wp"),we=[],g=[];for(let b=0;b<re.length;++b){const[e,l]=(0,O.max)([be[b],ye[b],ne[b]]);we.push([re[b],xe[b],se[b]][l]),g.push(e)}return{generated_text:we,scores:g,char_preds:re,bpe_preds:xe,wp_preds:se}}static async from_pretrained(...S){const X=await super.from_pretrained(...S),H=await j.AutoTokenizer.from_pretrained("Xenova/gpt2"),re=await j.AutoTokenizer.from_pretrained("Xenova/bert-base-uncased");return X.components={image_processor:X.image_processor,char_tokenizer:X.tokenizer,bpe_tokenizer:H,wp_tokenizer:re},X}async _call(S,X=null){const H=await this.image_processor(S);return X&&(H.labels=this.tokenizer(X).input_ids),H}}},"./src/models/mobilenet_v1/image_processing_mobilenet_v1.js":(x,C,d)=>{d.r(C),d.d(C,{MobileNetV1FeatureExtractor:()=>j,MobileNetV1ImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{}class j extends P{}},"./src/models/mobilenet_v2/image_processing_mobilenet_v2.js":(x,C,d)=>{d.r(C),d.d(C,{MobileNetV2FeatureExtractor:()=>j,MobileNetV2ImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{}class j extends P{}},"./src/models/mobilenet_v3/image_processing_mobilenet_v3.js":(x,C,d)=>{d.r(C),d.d(C,{MobileNetV3FeatureExtractor:()=>j,MobileNetV3ImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{}class j extends P{}},"./src/models/mobilenet_v4/image_processing_mobilenet_v4.js":(x,C,d)=>{d.r(C),d.d(C,{MobileNetV4FeatureExtractor:()=>j,MobileNetV4ImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{}class j extends P{}},"./src/models/mobilevit/image_processing_mobilevit.js":(x,C,d)=>{d.r(C),d.d(C,{MobileViTFeatureExtractor:()=>j,MobileViTImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{}class j extends P{}},"./src/models/moonshine/feature_extraction_moonshine.js":(x,C,d)=>{d.r(C),d.d(C,{MoonshineFeatureExtractor:()=>j});var k=d("./src/base/feature_extraction_utils.js"),P=d("./src/utils/tensor.js");class j extends k.FeatureExtractor{async _call(V){(0,k.validate_audio_inputs)(V,"MoonshineFeatureExtractor"),V instanceof Float64Array&&(V=new Float32Array(V));const q=[1,V.length];return{input_values:new P.Tensor("float32",V,q)}}}},"./src/models/moonshine/processing_moonshine.js":(x,C,d)=>{d.r(C),d.d(C,{MoonshineProcessor:()=>O});var k=d("./src/models/auto/feature_extraction_auto.js"),P=d("./src/tokenizers.js"),j=d("./src/base/processing_utils.js");class O extends j.Processor{static tokenizer_class=P.AutoTokenizer;static feature_extractor_class=k.AutoFeatureExtractor;async _call(q){return await this.feature_extractor(q)}}},"./src/models/nougat/image_processing_nougat.js":(x,C,d)=>{d.r(C),d.d(C,{NougatImageProcessor:()=>P});var k=d("./src/models/donut/image_processing_donut.js");class P extends k.DonutImageProcessor{}},"./src/models/owlv2/image_processing_owlv2.js":(x,C,d)=>{d.r(C),d.d(C,{Owlv2ImageProcessor:()=>P});var k=d("./src/models/owlvit/image_processing_owlvit.js");class P extends k.OwlViTImageProcessor{}},"./src/models/owlvit/image_processing_owlvit.js":(x,C,d)=>{d.r(C),d.d(C,{OwlViTFeatureExtractor:()=>j,OwlViTImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{post_process_object_detection(...V){return(0,k.post_process_object_detection)(...V)}}class j extends P{}},"./src/models/owlvit/processing_owlvit.js":(x,C,d)=>{d.r(C),d.d(C,{OwlViTProcessor:()=>O});var k=d("./src/base/processing_utils.js"),P=d("./src/models/auto/image_processing_auto.js"),j=d("./src/tokenizers.js");class O extends k.Processor{static tokenizer_class=j.AutoTokenizer;static image_processor_class=P.AutoImageProcessor}},"./src/models/paligemma/processing_paligemma.js":(x,C,d)=>{d.r(C),d.d(C,{PaliGemmaProcessor:()=>V});var k=d("./src/base/processing_utils.js"),P=d("./src/models/auto/image_processing_auto.js"),j=d("./src/tokenizers.js");const O="<image>";class V extends k.Processor{static tokenizer_class=j.AutoTokenizer;static image_processor_class=P.AutoImageProcessor;static uses_processor_config=!1;async _call(R,S=null,X={}){S||(console.warn("You are using PaliGemma without a text prefix. It will perform as a picture-captioning model."),S=""),Array.isArray(R)||(R=[R]),Array.isArray(S)||(S=[S]);const H=this.tokenizer.bos_token,re=this.image_processor.config.image_seq_length;let be;S.some(ye=>ye.includes(O))?be=S.map(ye=>{const se=ye.replaceAll(O,O.repeat(re)),ne=se.lastIndexOf(O),we=ne===-1?0:ne+7;return se.slice(0,we)+H+se.slice(we)+`
`}):(console.warn("You are passing both `text` and `images` to `PaliGemmaProcessor`. The processor expects special image tokens in the text, as many tokens as there are images per each text. It is recommended to add `<image>` tokens in the very beginning of your text. For this call, we will infer how many images each text has and add special tokens."),be=S.map(ye=>function(se,ne,we,g,b){return`${g.repeat(we*b)}${ne}${se}
`}(ye,H,re,O,R.length)));const xe=this.tokenizer(be,X);return{...await this.image_processor(R,X),...xe}}}},"./src/models/phi3_v/image_processing_phi3_v.js":(x,C,d)=>{d.r(C),d.d(C,{Phi3VImageProcessor:()=>S});var k=d("./src/base/image_processors_utils.js"),P=d("./src/utils/tensor.js");const j=336,O=[2,3],{ceil:V,floor:q,sqrt:R}=Math;class S extends k.ImageProcessor{constructor(H){super({...H,do_normalize:!0,do_pad:!0,pad_size:"custom",do_convert_rgb:!0,do_resize:!0}),this._num_crops=H.num_crops}calc_num_image_tokens_from_image_size(H,re){const{num_img_tokens:be}=this.config;return q((q(re/j)*q(H/j)+1)*be+1+(q(re/j)+1)*R(be))}get_resize_output_image_size(H,re){const be=this._num_crops,[xe,ye]=H.size;let se=xe/ye,ne=1;for(;ne*Math.ceil(ne/se)<=be;)ne+=1;ne-=1;const we=Math.floor(336*ne);return[we,Math.floor(we/se)]}pad_image(H,re,be,xe={}){const[ye,se]=re,ne=j*V(ye/j),we=j*V(se/j),g=[1,1,1].map((b,e)=>(b-this.image_mean[e])/this.image_std[e]);return super.pad_image(H,re,{width:we,height:ne},{center:!0,constant_values:g,...xe})}async _call(H,{num_crops:re=null}={}){if(this._num_crops=re??=this.config.num_crops,re<4||R(re)%1!=0)throw new Error("num_crops must be a square number >= 4");Array.isArray(H)||(H=[H]);const be=H.length,xe=await Promise.all(H.map(b=>this.preprocess(b))),ye=xe.map(b=>b.original_size),se=xe.map(b=>b.reshaped_input_size),ne=[];for(const{pixel_values:b}of xe){b.unsqueeze_(0);const[e,l]=b.dims.slice(-2),M=await(0,P.interpolate_4d)(b,{size:[j,j],mode:"bicubic"});if(re>0){const ue=[],D=R(re),z=q(l/D),Re=q(e/D);for(let Ye=0;Ye<D;++Ye)for(let Je=0;Je<D;++Je){let AA,xA,GA,Ge;Ye===D-1?(xA=e-Re,Ge=e):(xA=Ye*Re,Ge=(Ye+1)*Re),Je===D-1?(AA=l-z,GA=l):(AA=Je*z,GA=(Je+1)*z);const Ie=[xA,AA],ze=[Ge,GA],Ue=await(0,P.slice)(b,Ie,ze,O);ue.push(Ue)}const Ne=await(0,P.interpolate_4d)((0,P.cat)(ue,0),{size:[j,j],mode:"bicubic"});ne.push((0,P.cat)([M,Ne],0))}else ne.push(M)}const we=(0,P.stack)(ne,0),g=se.map(b=>b.map(e=>j*V(e/j)));return{pixel_values:we,original_sizes:ye,reshaped_input_sizes:se,image_sizes:new P.Tensor("int64",g.flat(),[be,2]),num_img_tokens:g.map(([b,e])=>this.calc_num_image_tokens_from_image_size(e,b))}}}},"./src/models/phi3_v/processing_phi3_v.js":(x,C,d)=>{d.r(C),d.d(C,{Phi3VProcessor:()=>q});var k=d("./src/base/processing_utils.js"),P=d("./src/models/auto/image_processing_auto.js"),j=d("./src/tokenizers.js");d("./src/utils/image.js");const O="<|image|>",V=/<\|image_\d+\|>/g;class q extends k.Processor{static image_processor_class=P.AutoImageProcessor;static tokenizer_class=j.AutoTokenizer;async _call(S,X=null,{padding:H=!0,truncation:re=!0,num_crops:be=null}={}){let xe,ye;if(Array.isArray(S)||(S=[S]),X){ye=await this.image_processor(X,{num_crops:be});const{num_img_tokens:se}=ye,ne=S.map((g,b)=>g.split(V).join(O.repeat(se[b])));xe=this.tokenizer(ne,{padding:H,truncation:re});const 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k.FeatureExtractor{constructor(V){super(V);const q=this.config.sampling_rate,R=(0,P.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(q/2),q,null,"kaldi",!0);for(let S=0;S<R.length;++S)R[S].push(0);this.mel_filters=R,this.window=(0,P.window_function)(400,"hamming",{periodic:!1}),this.min_num_frames=this.config.min_num_frames}async _extract_fbank_features(V){return V=V.map(q=>32768*q),(0,P.spectrogram)(V,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,transpose:!0,min_num_frames:this.min_num_frames})}async _call(V){(0,k.validate_audio_inputs)(V,"WeSpeakerFeatureExtractor");const q=(await this._extract_fbank_features(V)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const R=q.mean(1).data,S=q.data,[X,H,re]=q.dims;for(let be=0;be<X;++be){const xe=be*H*re,ye=be*re;for(let se=0;se<H;++se){const ne=xe+se*re;for(let we=0;we<re;++we)S[ne+we]-=R[ye+we]}}}return{input_features:q}}}},"./src/models/whisper/common_whisper.js":(x,C,d)=>{d.r(C),d.d(C,{WHISPER_LANGUAGE_MAPPING:()=>P,WHISPER_TO_LANGUAGE_CODE_MAPPING:()=>j,whisper_language_to_code:()=>O});const k=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],P=new Map(k),j=new Map([...k.map(([V,q])=>[q,V]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function O(V){V=V.toLowerCase();let q=j.get(V);if(q===void 0){if(!P.has(V)){const R=V.length===2?P.keys():P.values();throw new Error(`Language "${V}" is not supported. Must be one of: ${JSON.stringify(R)}`)}q=V}return q}},"./src/models/whisper/feature_extraction_whisper.js":(x,C,d)=>{d.r(C),d.d(C,{WhisperFeatureExtractor:()=>O});var k=d("./src/base/feature_extraction_utils.js");d("./src/utils/tensor.js");var P=d("./src/utils/audio.js"),j=d("./src/utils/maths.js");class O extends k.FeatureExtractor{constructor(q){super(q),this.config.mel_filters??=(0,P.mel_filter_bank)(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=(0,P.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(q){const R=await(0,P.spectrogram)(q,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),S=R.data,X=(0,j.max)(S)[0];for(let H=0;H<S.length;++H)S[H]=(Math.max(S[H],X-8)+4)/4;return R}async _call(q){let R;return(0,k.validate_audio_inputs)(q,"WhisperFeatureExtractor"),q.length>this.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),R=q.slice(0,this.config.n_samples)):(R=new Float32Array(this.config.n_samples),R.set(q)),{input_features:(await this._extract_fbank_features(R)).unsqueeze_(0)}}}},"./src/models/whisper/generation_whisper.js":(x,C,d)=>{d.r(C),d.d(C,{WhisperGenerationConfig:()=>P});var k=d("./src/generation/configuration_utils.js");class P extends k.GenerationConfig{return_timestamps=null;return_token_timestamps=null;num_frames=null;alignment_heads=null;task=null;language=null;no_timestamps_token_id=null;prompt_ids=null;is_multilingual=null;lang_to_id=null;task_to_id=null;max_initial_timestamp_index=1}},"./src/models/whisper/processing_whisper.js":(x,C,d)=>{d.r(C),d.d(C,{WhisperProcessor:()=>O});var k=d("./src/models/auto/feature_extraction_auto.js"),P=d("./src/tokenizers.js"),j=d("./src/base/processing_utils.js");class O extends j.Processor{static tokenizer_class=P.AutoTokenizer;static feature_extractor_class=k.AutoFeatureExtractor;async _call(q){return await this.feature_extractor(q)}}},"./src/models/yolos/image_processing_yolos.js":(x,C,d)=>{d.r(C),d.d(C,{YolosFeatureExtractor:()=>j,YolosImageProcessor:()=>P});var k=d("./src/base/image_processors_utils.js");class P extends k.ImageProcessor{post_process_object_detection(...V){return(0,k.post_process_object_detection)(...V)}}class j extends P{}},"./src/ops/registry.js":(x,C,d)=>{d.r(C),d.d(C,{TensorOpRegistry:()=>q});var k=d("./src/backends/onnx.js"),P=d("./src/utils/tensor.js"),j=d("./src/env.js");const O=j.apis.IS_BROWSER_ENV||j.apis.IS_WEBWORKER_ENV,V=async(R,S,X)=>{const H=await(0,k.createInferenceSession)(new Uint8Array(R),S);let re=Promise.resolve();return async be=>{const xe=(0,k.isONNXProxy)(),ye=Object.fromEntries(Object.entries(be).map(([ne,we])=>[ne,(xe?we.clone():we).ort_tensor])),se=await(re=O?re.then(()=>H.run(ye)):H.run(ye));return Array.isArray(X)?X.map(ne=>new P.Tensor(se[ne])):new P.Tensor(se[X])}};class q{static session_options={};static get nearest_interpolate_4d(){return this._nearest_interpolate_4d||(this._nearest_interpolate_4d=V([8,10,18,0,58,129,1,10,41,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,18,10,4,109,111,100,101,34,7,110,101,97,114,101,115,116,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,21],this.session_options,"y")),this._nearest_interpolate_4d}static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=V([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=V([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||(this._matmul=V([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,"c")),this._matmul}static get stft(){return this._stft||(this._stft=V([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,"o")),this._stft}static get rfft(){return this._rfft||(this._rfft=V([8,9,18,0,58,97,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,21,10,1,120,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,90,11,10,1,97,18,6,10,4,8,7,18,0,98,21,10,1,121,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,66,2,16,20],this.session_options,"y")),this._rfft}static get top_k(){return this._top_k||(this._top_k=V([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}static get slice(){return this._slice||(this._slice=V([8,7,18,0,58,96,10,25,10,1,120,10,1,115,10,1,101,10,1,97,10,1,116,18,1,121,34,5,83,108,105,99,101,18,1,114,90,9,10,1,120,18,4,10,2,8,1,90,9,10,1,115,18,4,10,2,8,7,90,9,10,1,101,18,4,10,2,8,7,90,9,10,1,97,18,4,10,2,8,7,90,9,10,1,116,18,4,10,2,8,7,98,9,10,1,121,18,4,10,2,8,1,66,2,16,13],this.session_options,"y")),this._slice}}},"./src/pipelines.js":(x,C,d)=>{d.r(C),d.d(C,{AudioClassificationPipeline:()=>Re,AutomaticSpeechRecognitionPipeline:()=>Ye,DepthEstimationPipeline:()=>kA,DocumentQuestionAnsweringPipeline:()=>ze,FeatureExtractionPipeline:()=>D,FillMaskPipeline:()=>we,ImageClassificationPipeline:()=>AA,ImageFeatureExtractionPipeline:()=>z,ImageSegmentationPipeline:()=>xA,ImageToImagePipeline:()=>Pe,ImageToTextPipeline:()=>Je,ObjectDetectionPipeline:()=>Ge,Pipeline:()=>xe,QuestionAnsweringPipeline:()=>ne,SummarizationPipeline:()=>b,Text2TextGenerationPipeline:()=>g,TextClassificationPipeline:()=>ye,TextGenerationPipeline:()=>M,TextToAudioPipeline:()=>Ue,TokenClassificationPipeline:()=>se,TranslationPipeline:()=>e,ZeroShotAudioClassificationPipeline:()=>Ne,ZeroShotClassificationPipeline:()=>ue,ZeroShotImageClassificationPipeline:()=>GA,ZeroShotObjectDetectionPipeline:()=>Ie,pipeline:()=>yA});var k=d("./src/tokenizers.js"),P=d("./src/models.js"),j=d("./src/models/auto/processing_auto.js");d("./src/base/processing_utils.js");var O=d("./src/utils/generic.js"),V=d("./src/utils/core.js"),q=d("./src/utils/maths.js"),R=d("./src/utils/audio.js"),S=d("./src/utils/tensor.js"),X=d("./src/utils/image.js");async function H(nA){return Array.isArray(nA)||(nA=[nA]),await Promise.all(nA.map(Fe=>X.RawImage.read(Fe)))}async function re(nA,Fe){return Array.isArray(nA)||(nA=[nA]),await Promise.all(nA.map(Ze=>typeof Ze=="string"||Ze instanceof URL?(0,R.read_audio)(Ze,Fe):Ze instanceof Float64Array?new Float32Array(Ze):Ze))}function be(nA,Fe){Fe&&(nA=nA.map(pA=>0|pA));const[Ze,mA,CA,Te]=nA;return{xmin:Ze,ymin:mA,xmax:CA,ymax:Te}}class xe extends O.Callable{constructor({task:Fe,model:Ze,tokenizer:mA=null,processor:CA=null}){super(),this.task=Fe,this.model=Ze,this.tokenizer=mA,this.processor=CA}async dispose(){await this.model.dispose()}}class ye extends xe{constructor(Fe){super(Fe)}async _call(Fe,{top_k:Ze=1}={}){const mA=this.tokenizer(Fe,{padding:!0,truncation:!0}),CA=await this.model(mA),Te=this.model.config.problem_type==="multi_label_classification"?SA=>SA.sigmoid():SA=>new S.Tensor("float32",(0,q.softmax)(SA.data),SA.dims),pA=this.model.config.id2label,EA=[];for(const SA of CA.logits){const YA=Te(SA),J=await(0,S.topk)(YA,Ze),OA=J[0].tolist(),ge=J[1].tolist().map(($,me)=>({label:pA?pA[$]:`LABEL_${$}`,score:OA[me]}));Ze===1?EA.push(...ge):EA.push(ge)}return Array.isArray(Fe)||Ze===1?EA:EA[0]}}class se extends xe{constructor(Fe){super(Fe)}async _call(Fe,{ignore_labels:Ze=["O"]}={}){const mA=Array.isArray(Fe),CA=this.tokenizer(mA?Fe:[Fe],{padding:!0,truncation:!0}),Te=(await this.model(CA)).logits,pA=this.model.config.id2label,EA=[];for(let SA=0;SA<Te.dims[0];++SA){const YA=CA.input_ids[SA],J=Te[SA],OA=[];for(let ge=0;ge<J.dims[0];++ge){const $=J[ge],me=(0,q.max)($.data)[1],Se=pA?pA[me]:`LABEL_${me}`;if(Ze.includes(Se))continue;const qe=this.tokenizer.decode([YA[ge].item()],{skip_special_tokens:!0});if(qe==="")continue;const dA=(0,q.softmax)($.data);OA.push({entity:Se,score:dA[me],index:ge,word:qe})}EA.push(OA)}return mA?EA:EA[0]}}class ne extends xe{constructor(Fe){super(Fe)}async _call(Fe,Ze,{top_k:mA=1}={}){const CA=this.tokenizer(Fe,{text_pair:Ze,padding:!0,truncation:!0}),{start_logits:Te,end_logits:pA}=await this.model(CA),EA=CA.input_ids.tolist(),SA=CA.attention_mask.tolist(),YA=this.tokenizer.all_special_ids,J=[];for(let OA=0;OA<Te.dims[0];++OA){const ge=EA[OA],$=ge.findIndex(BA=>BA==this.tokenizer.sep_token_id);SA[OA].map((BA,QA)=>BA==1&&(QA===0||QA>$&&YA.findIndex(TA=>TA==ge[QA])===-1));const me=Te[OA].tolist(),Se=pA[OA].tolist();for(let BA=1;BA<me.length;++BA)(SA[OA]==0||BA<=$||YA.findIndex(QA=>QA==ge[BA])!==-1)&&(me[BA]=-1/0,Se[BA]=-1/0);const qe=(0,q.softmax)(me).map((BA,QA)=>[BA,QA]),dA=(0,q.softmax)(Se).map((BA,QA)=>[BA,QA]);qe[0][0]=0,dA[0][0]=0;const oA=(0,V.product)(qe,dA).filter(BA=>BA[0][1]<=BA[1][1]).map(BA=>[BA[0][1],BA[1][1],BA[0][0]*BA[1][0]]).sort((BA,QA)=>QA[2]-BA[2]);for(let BA=0;BA<Math.min(oA.length,mA);++BA){const[QA,TA,Mt]=oA[BA],Tt=ge.slice(QA,TA+1),ut=this.tokenizer.decode(Tt,{skip_special_tokens:!0});J.push({answer:ut,score:Mt})}}return mA===1?J[0]:J}}class we extends xe{constructor(Fe){super(Fe)}async _call(Fe,{top_k:Ze=5}={}){const mA=this.tokenizer(Fe,{padding:!0,truncation:!0}),{logits:CA}=await this.model(mA),Te=[],pA=mA.input_ids.tolist();for(let EA=0;EA<pA.length;++EA){const SA=pA[EA],YA=SA.findIndex(me=>me==this.tokenizer.mask_token_id);if(YA===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const J=CA[EA][YA],OA=await(0,S.topk)(new S.Tensor("float32",(0,q.softmax)(J.data),J.dims),Ze),ge=OA[0].tolist(),$=OA[1].tolist();Te.push($.map((me,Se)=>{const qe=SA.slice();return qe[YA]=me,{score:ge[Se],token:Number(me),token_str:this.tokenizer.decode([me]),sequence:this.tokenizer.decode(qe,{skip_special_tokens:!0})}}))}return Array.isArray(Fe)?Te:Te[0]}}class g extends xe{_key="generated_text";constructor(Fe){super(Fe)}async _call(Fe,Ze={}){Array.isArray(Fe)||(Fe=[Fe]),this.model.config.prefix&&(Fe=Fe.map(SA=>this.model.config.prefix+SA));const mA=this.model.config.task_specific_params;mA&&mA[this.task]&&mA[this.task].prefix&&(Fe=Fe.map(SA=>mA[this.task].prefix+SA));const CA=this.tokenizer,Te={padding:!0,truncation:!0};let pA;pA=this instanceof e&&"_build_translation_inputs"in CA?CA._build_translation_inputs(Fe,Te,Ze):CA(Fe,Te);const EA=await this.model.generate({...pA,...Ze});return CA.batch_decode(EA,{skip_special_tokens:!0}).map(SA=>({[this._key]:SA}))}}class b extends g{_key="summary_text";constructor(Fe){super(Fe)}}class e extends g{_key="translation_text";constructor(Fe){super(Fe)}}function l(nA){return Array.isArray(nA)&&nA.every(Fe=>"role"in Fe&&"content"in Fe)}class M extends xe{constructor(Fe){super(Fe)}async _call(Fe,Ze={}){let mA,CA=!1,Te=!1;if(typeof Fe=="string")mA=Fe=[Fe];else if(Array.isArray(Fe)&&Fe.every($=>typeof $=="string"))CA=!0,mA=Fe;else{if(l(Fe))Fe=[Fe];else{if(!Array.isArray(Fe)||!Fe.every(l))throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");CA=!0}Te=!0,mA=Fe.map($=>this.tokenizer.apply_chat_template($,{tokenize:!1,add_generation_prompt:!0}))}const pA=Ze.add_special_tokens??!1,EA=!Te&&(Ze.return_full_text??!0);this.tokenizer.padding_side="left";const SA=this.tokenizer(mA,{add_special_tokens:pA,padding:!0,truncation:!0}),YA=await this.model.generate({...SA,...Ze}),J=this.tokenizer.batch_decode(YA,{skip_special_tokens:!0});let OA;!EA&&SA.input_ids.dims.at(-1)>0&&(OA=this.tokenizer.batch_decode(SA.input_ids,{skip_special_tokens:!0}).map($=>$.length));const ge=Array.from({length:Fe.length},$=>[]);for(let $=0;$<J.length;++$){const me=Math.floor($/YA.dims[0]*Fe.length);OA&&(J[$]=J[$].slice(OA[me])),ge[me].push({generated_text:Te?[...Fe[me],{role:"assistant",content:J[$]}]:J[$]})}return CA||ge.length!==1?ge:ge[0]}}class ue extends xe{constructor(Fe){super(Fe),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([Ze,mA])=>[Ze.toLowerCase(),mA])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0&&(console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,this.contradiction_id===void 0&&(console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(Fe,Ze,{hypothesis_template:mA="This example is {}.",multi_label:CA=!1}={}){const Te=Array.isArray(Fe);Te||(Fe=[Fe]),Array.isArray(Ze)||(Ze=[Ze]);const pA=Ze.map(YA=>mA.replace("{}",YA)),EA=CA||Ze.length===1,SA=[];for(const YA of Fe){const J=[];for(const $ of pA){const me=this.tokenizer(YA,{text_pair:$,padding:!0,truncation:!0}),Se=await this.model(me);EA?J.push([Se.logits.data[this.contradiction_id],Se.logits.data[this.entailment_id]]):J.push(Se.logits.data[this.entailment_id])}const OA=EA?J.map($=>(0,q.softmax)($)[1]):(0,q.softmax)(J),ge=OA.map(($,me)=>[$,me]).sort(($,me)=>me[0]-$[0]);SA.push({sequence:YA,labels:ge.map($=>Ze[$[1]]),scores:ge.map($=>$[0])})}return Te?SA:SA[0]}}class D extends xe{constructor(Fe){super(Fe)}async _call(Fe,{pooling:Ze="none",normalize:mA=!1,quantize:CA=!1,precision:Te="binary"}={}){const pA=this.tokenizer(Fe,{padding:!0,truncation:!0}),EA=await this.model(pA);let SA=EA.last_hidden_state??EA.logits??EA.token_embeddings;if(Ze!=="none")if(Ze==="mean")SA=(0,S.mean_pooling)(SA,pA.attention_mask);else{if(Ze!=="cls")throw Error(`Pooling method '${Ze}' not supported.`);SA=SA.slice(null,0)}return mA&&(SA=SA.normalize(2,-1)),CA&&(SA=(0,S.quantize_embeddings)(SA,Te)),SA}}class z extends xe{constructor(Fe){super(Fe)}async _call(Fe,{pool:Ze=null}={}){const mA=await H(Fe),{pixel_values:CA}=await this.processor(mA),Te=await this.model({pixel_values:CA});let pA;if(Ze){if(!("pooler_output"in Te))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");pA=Te.pooler_output}else pA=Te.last_hidden_state??Te.logits??Te.image_embeds;return pA}}class Re extends xe{constructor(Fe){super(Fe)}async _call(Fe,{top_k:Ze=5}={}){const mA=this.processor.feature_extractor.config.sampling_rate,CA=await re(Fe,mA),Te=this.model.config.id2label,pA=[];for(const EA of CA){const SA=await this.processor(EA),YA=(await this.model(SA)).logits[0],J=await(0,S.topk)(new S.Tensor("float32",(0,q.softmax)(YA.data),YA.dims),Ze),OA=J[0].tolist(),ge=J[1].tolist().map(($,me)=>({label:Te?Te[$]:`LABEL_${$}`,score:OA[me]}));pA.push(ge)}return Array.isArray(Fe)?pA:pA[0]}}class Ne extends xe{constructor(Fe){super(Fe)}async _call(Fe,Ze,{hypothesis_template:mA="This is a sound of {}."}={}){const CA=!Array.isArray(Fe);CA&&(Fe=[Fe]);const Te=Ze.map(J=>mA.replace("{}",J)),pA=this.tokenizer(Te,{padding:!0,truncation:!0}),EA=this.processor.feature_extractor.config.sampling_rate,SA=await re(Fe,EA),YA=[];for(const J of SA){const OA=await this.processor(J),ge=await this.model({...pA,...OA}),$=(0,q.softmax)(ge.logits_per_audio.data);YA.push([...$].map((me,Se)=>({score:me,label:Ze[Se]})))}return CA?YA[0]:YA}}class Ye extends xe{constructor(Fe){super(Fe)}async _call(Fe,Ze={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(Fe,Ze);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(Fe,Ze);case"moonshine":return this._call_moonshine(Fe,Ze);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(Fe,Ze){Ze.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),Ze.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const mA=!Array.isArray(Fe);mA&&(Fe=[Fe]);const CA=this.processor.feature_extractor.config.sampling_rate,Te=await re(Fe,CA),pA=[];for(const EA of Te){const SA=await this.processor(EA),YA=(await this.model(SA)).logits[0],J=[];for(const ge of YA)J.push((0,q.max)(ge.data)[1]);const OA=this.tokenizer.decode(J);pA.push({text:OA})}return mA?pA[0]:pA}async _call_whisper(Fe,Ze){const mA=Ze.return_timestamps??!1,CA=Ze.chunk_length_s??0,Te=Ze.force_full_sequences??!1;let pA=Ze.stride_length_s??null;const EA={...Ze};mA==="word"&&(EA.return_token_timestamps=!0,EA.return_timestamps=!1);const SA=!Array.isArray(Fe);SA&&(Fe=[Fe]);const YA=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,J=this.processor.feature_extractor.config.hop_length,OA=this.processor.feature_extractor.config.sampling_rate,ge=await re(Fe,OA),$=[];for(const me of ge){let Se=[];if(CA>0){if(pA===null)pA=CA/6;else if(CA<=pA)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const oA=OA*CA,BA=OA*pA,QA=oA-2*BA;let TA=0;for(;;){const Mt=TA+oA,Tt=me.subarray(TA,Mt),ut=await this.processor(Tt),Ct=TA===0,$t=Mt>=me.length;if(Se.push({stride:[Tt.length,Ct?0:BA,$t?0:BA],input_features:ut.input_features,is_last:$t}),$t)break;TA+=QA}}else Se=[{stride:[me.length,0,0],input_features:(await this.processor(me)).input_features,is_last:!0}];for(const oA of Se){EA.num_frames=Math.floor(oA.stride[0]/J);const BA=await this.model.generate({inputs:oA.input_features,...EA});mA==="word"?(oA.tokens=BA.sequences.tolist()[0],oA.token_timestamps=BA.token_timestamps.tolist()[0].map(QA=>(0,q.round)(QA,2))):oA.tokens=BA[0].tolist(),oA.stride=oA.stride.map(QA=>QA/OA)}const[qe,dA]=this.tokenizer._decode_asr(Se,{time_precision:YA,return_timestamps:mA,force_full_sequences:Te});$.push({text:qe,...dA})}return SA?$[0]:$}async _call_moonshine(Fe,Ze){const mA=!Array.isArray(Fe);mA&&(Fe=[Fe]);const CA=this.processor.feature_extractor.config.sampling_rate,Te=await re(Fe,CA),pA=[];for(const EA of Te){const SA=await this.processor(EA),YA=6*Math.floor(EA.length/CA),J=await this.model.generate({max_new_tokens:YA,...Ze,...SA}),OA=this.processor.batch_decode(J,{skip_special_tokens:!0})[0];pA.push({text:OA})}return mA?pA[0]:pA}}class Je extends xe{constructor(Fe){super(Fe)}async _call(Fe,Ze={}){const mA=Array.isArray(Fe),CA=await H(Fe),{pixel_values:Te}=await this.processor(CA),pA=[];for(const EA of Te){EA.dims=[1,...EA.dims];const SA=await this.model.generate({inputs:EA,...Ze}),YA=this.tokenizer.batch_decode(SA,{skip_special_tokens:!0}).map(J=>({generated_text:J.trim()}));pA.push(YA)}return mA?pA:pA[0]}}class AA extends xe{constructor(Fe){super(Fe)}async _call(Fe,{top_k:Ze=5}={}){const mA=await H(Fe),{pixel_values:CA}=await this.processor(mA),Te=await this.model({pixel_values:CA}),pA=this.model.config.id2label,EA=[];for(const SA of Te.logits){const YA=await(0,S.topk)(new S.Tensor("float32",(0,q.softmax)(SA.data),SA.dims),Ze),J=YA[0].tolist(),OA=YA[1].tolist().map((ge,$)=>({label:pA?pA[ge]:`LABEL_${ge}`,score:J[$]}));EA.push(OA)}return Array.isArray(Fe)?EA:EA[0]}}class xA extends xe{constructor(Fe){super(Fe),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(Fe,{threshold:Ze=.5,mask_threshold:mA=.5,overlap_mask_area_threshold:CA=.8,label_ids_to_fuse:Te=null,target_sizes:pA=null,subtask:EA=null}={}){if(Array.isArray(Fe)&&Fe.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const SA=await H(Fe),YA=SA.map(qe=>[qe.height,qe.width]),{pixel_values:J,pixel_mask:OA}=await this.processor(SA),ge=await this.model({pixel_values:J,pixel_mask:OA});let $=null;if(EA!==null)$=this.subtasks_mapping[EA];else for(let[qe,dA]of Object.entries(this.subtasks_mapping))if(dA in this.processor.image_processor){$=this.processor.image_processor[dA].bind(this.processor.image_processor),EA=qe;break}const me=this.model.config.id2label,Se=[];if(EA==="panoptic"||EA==="instance"){const qe=$(ge,Ze,mA,CA,Te,pA??YA)[0],dA=qe.segmentation;for(const oA of qe.segments_info){const BA=new Uint8ClampedArray(dA.data.length);for(let TA=0;TA<dA.data.length;++TA)dA.data[TA]===oA.id&&(BA[TA]=255);const QA=new X.RawImage(BA,dA.dims[1],dA.dims[0],1);Se.push({score:oA.score,label:me[oA.label_id],mask:QA})}}else{if(EA!=="semantic")throw Error(`Subtask ${EA} not supported.`);{const{segmentation:qe,labels:dA}=$(ge,pA??YA)[0];for(const oA of dA){const BA=new Uint8ClampedArray(qe.data.length);for(let TA=0;TA<qe.data.length;++TA)qe.data[TA]===oA&&(BA[TA]=255);const QA=new X.RawImage(BA,qe.dims[1],qe.dims[0],1);Se.push({score:null,label:me[oA],mask:QA})}}}return Se}}class GA extends xe{constructor(Fe){super(Fe)}async _call(Fe,Ze,{hypothesis_template:mA="This is a photo of {}"}={}){const CA=Array.isArray(Fe),Te=await H(Fe),pA=Ze.map(ge=>mA.replace("{}",ge)),EA=this.tokenizer(pA,{padding:this.model.config.model_type!=="siglip"||"max_length",truncation:!0}),{pixel_values:SA}=await this.processor(Te),YA=await this.model({...EA,pixel_values:SA}),J=this.model.config.model_type==="siglip"?ge=>ge.sigmoid().data:ge=>(0,q.softmax)(ge.data),OA=[];for(const ge of YA.logits_per_image){const $=[...J(ge)].map((me,Se)=>({score:me,label:Ze[Se]}));$.sort((me,Se)=>Se.score-me.score),OA.push($)}return CA?OA:OA[0]}}class Ge extends xe{constructor(Fe){super(Fe)}async _call(Fe,{threshold:Ze=.9,percentage:mA=!1}={}){const CA=Array.isArray(Fe);if(CA&&Fe.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const Te=await H(Fe),pA=mA?null:Te.map($=>[$.height,$.width]),{pixel_values:EA,pixel_mask:SA}=await this.processor(Te),YA=await this.model({pixel_values:EA,pixel_mask:SA}),J=this.processor.image_processor.post_process_object_detection(YA,Ze,pA),OA=this.model.config.id2label,ge=J.map($=>$.boxes.map((me,Se)=>({score:$.scores[Se],label:OA[$.classes[Se]],box:be(me,!mA)})));return CA?ge:ge[0]}}class Ie extends xe{constructor(Fe){super(Fe)}async _call(Fe,Ze,{threshold:mA=.1,top_k:CA=null,percentage:Te=!1}={}){const pA=Array.isArray(Fe),EA=await H(Fe),SA=this.tokenizer(Ze,{padding:!0,truncation:!0}),YA=await this.processor(EA),J=[];for(let OA=0;OA<EA.length;++OA){const ge=EA[OA],$=Te?null:[[ge.height,ge.width]],me=YA.pixel_values[OA].unsqueeze_(0),Se=await this.model({...SA,pixel_values:me});let qe;if("post_process_grounded_object_detection"in this.processor){const dA=this.processor.post_process_grounded_object_detection(Se,SA.input_ids,{box_threshold:mA,text_threshold:mA,target_sizes:$})[0];qe=dA.boxes.map((oA,BA)=>({score:dA.scores[BA],label:dA.labels[BA],box:be(oA,!Te)}))}else{const dA=this.processor.image_processor.post_process_object_detection(Se,mA,$,!0)[0];qe=dA.boxes.map((oA,BA)=>({score:dA.scores[BA],label:Ze[dA.classes[BA]],box:be(oA,!Te)}))}qe.sort((dA,oA)=>oA.score-dA.score),CA!==null&&(qe=qe.slice(0,CA)),J.push(qe)}return pA?J:J[0]}}class ze extends xe{constructor(Fe){super(Fe)}async _call(Fe,Ze,mA={}){const CA=(await H(Fe))[0],{pixel_values:Te}=await this.processor(CA),pA=`<s_docvqa><s_question>${Ze}</s_question><s_answer>`,EA=this.tokenizer(pA,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,SA=await this.model.generate({inputs:Te,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:EA,...mA}),YA=this.tokenizer.batch_decode(SA)[0].match(/<s_answer>(.*?)<\/s_answer>/);let J=null;return YA&&YA.length>=2&&(J=YA[1].trim()),[{answer:J}]}}class Ue extends xe{DEFAULT_VOCODER_ID="Xenova/speecht5_hifigan";constructor(Fe){super(Fe),this.vocoder=Fe.vocoder??null}async _call(Fe,{speaker_embeddings:Ze=null}={}){return this.processor?this._call_text_to_spectrogram(Fe,{speaker_embeddings:Ze}):this._call_text_to_waveform(Fe)}async _call_text_to_waveform(Fe){const Ze=this.tokenizer(Fe,{padding:!0,truncation:!0}),{waveform:mA}=await this.model(Ze),CA=this.model.config.sampling_rate;return new R.RawAudio(mA.data,CA)}async _call_text_to_spectrogram(Fe,{speaker_embeddings:Ze}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await P.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof Ze=="string"||Ze instanceof URL)&&(Ze=new Float32Array(await(await fetch(Ze)).arrayBuffer())),Ze instanceof Float32Array)Ze=new S.Tensor("float32",Ze,[1,Ze.length]);else if(!(Ze instanceof S.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:mA}=this.tokenizer(Fe,{padding:!0,truncation:!0}),{waveform:CA}=await this.model.generate_speech(mA,Ze,{vocoder:this.vocoder}),Te=this.processor.feature_extractor.config.sampling_rate;return new R.RawAudio(CA.data,Te)}}class Pe extends xe{constructor(Fe){super(Fe)}async _call(Fe){const Ze=await H(Fe),mA=await this.processor(Ze),CA=await this.model(mA),Te=[];for(const pA of CA.reconstruction){const EA=pA.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");Te.push(X.RawImage.fromTensor(EA))}return Te.length>1?Te:Te[0]}}class kA extends xe{constructor(Fe){super(Fe)}async _call(Fe){const Ze=await H(Fe),mA=await this.processor(Ze),{predicted_depth:CA}=await this.model(mA),Te=[];for(let pA=0;pA<Ze.length;++pA){const EA=CA[pA],[SA,YA]=EA.dims.slice(-2),[J,OA]=Ze[pA].size,ge=(await(0,S.interpolate_4d)(EA.view(1,1,SA,YA),{size:[OA,J],mode:"bilinear"})).view(OA,J),$=ge.min().item(),me=ge.max().item(),Se=ge.sub($).div_(me-$).mul_(255).to("uint8").unsqueeze(0),qe=X.RawImage.fromTensor(Se);Te.push({predicted_depth:ge,depth:qe})}return Te.length>1?Te:Te[0]}}const aA=Object.freeze({"text-classification":{tokenizer:k.AutoTokenizer,pipeline:ye,model:P.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:k.AutoTokenizer,pipeline:se,model:P.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:k.AutoTokenizer,pipeline:ne,model:P.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:k.AutoTokenizer,pipeline:we,model:P.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:k.AutoTokenizer,pipeline:b,model:P.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:k.AutoTokenizer,pipeline:e,model:P.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:k.AutoTokenizer,pipeline:g,model:P.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:k.AutoTokenizer,pipeline:M,model:P.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:k.AutoTokenizer,pipeline:ue,model:P.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:Re,model:P.AutoModelForAudioClassification,processor:j.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:k.AutoTokenizer,pipeline:Ne,model:P.AutoModel,processor:j.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:k.AutoTokenizer,pipeline:Ye,model:[P.AutoModelForSpeechSeq2Seq,P.AutoModelForCTC],processor:j.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:k.AutoTokenizer,pipeline:Ue,model:[P.AutoModelForTextToWaveform,P.AutoModelForTextToSpectrogram],processor:[j.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:k.AutoTokenizer,pipeline:Je,model:P.AutoModelForVision2Seq,processor:j.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:AA,model:P.AutoModelForImageClassification,processor:j.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:xA,model:[P.AutoModelForImageSegmentation,P.AutoModelForSemanticSegmentation,P.AutoModelForUniversalSegmentation],processor:j.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:k.AutoTokenizer,pipeline:GA,model:P.AutoModel,processor:j.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:Ge,model:P.AutoModelForObjectDetection,processor:j.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:k.AutoTokenizer,pipeline:Ie,model:P.AutoModelForZeroShotObjectDetection,processor:j.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:k.AutoTokenizer,pipeline:ze,model:P.AutoModelForDocumentQuestionAnswering,processor:j.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:Pe,model:P.AutoModelForImageToImage,processor:j.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:kA,model:P.AutoModelForDepthEstimation,processor:j.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:k.AutoTokenizer,pipeline:D,model:P.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:j.AutoProcessor,pipeline:z,model:[P.AutoModelForImageFeatureExtraction,P.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),iA=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function yA(nA,Fe=null,{progress_callback:Ze=null,config:mA=null,cache_dir:CA=null,local_files_only:Te=!1,revision:pA="main",device:EA=null,dtype:SA=null,model_file_name:YA=null,session_options:J={}}={}){nA=iA[nA]??nA;const OA=aA[nA.split("_",1)[0]];if(!OA)throw Error(`Unsupported pipeline: ${nA}. Must be one of [${Object.keys(aA)}]`);Fe||(Fe=OA.default.model,console.log(`No model specified. Using default model: "${Fe}".`));const ge={progress_callback:Ze,config:mA,cache_dir:CA,local_files_only:Te,revision:pA,device:EA,dtype:SA,model_file_name:YA,session_options:J},$=new Map([["tokenizer",OA.tokenizer],["model",OA.model],["processor",OA.processor]]),me=await async function(Se,qe,dA){const oA=Object.create(null),BA=[];for(const[QA,TA]of Se.entries()){if(!TA)continue;let Mt;Mt=Array.isArray(TA)?new Promise(async(Tt,ut)=>{let Ct;for(const $t of TA){if($t===null)return void Tt(null);try{return void Tt(await $t.from_pretrained(qe,dA))}catch(Qr){if(Qr.message?.includes("Unsupported model type"))Ct=Qr;else{if(!Qr.message?.includes("Could not locate file"))return void ut(Qr);Ct=Qr}}}ut(Ct)}):TA.from_pretrained(qe,dA),oA[QA]=Mt,BA.push(Mt)}await Promise.all(BA);for(const[QA,TA]of Object.entries(oA))oA[QA]=await TA;return oA}($,Fe,ge);return me.task=nA,(0,V.dispatchCallback)(Ze,{status:"ready",task:nA,model:Fe}),new OA.pipeline(me)}},"./src/tokenizers.js":(x,C,d)=>{d.r(C),d.d(C,{AlbertTokenizer:()=>Oa,AutoTokenizer:()=>Hs,BartTokenizer:()=>Ea,BertTokenizer:()=>Ka,BlenderbotSmallTokenizer:()=>Pt,BlenderbotTokenizer:()=>wt,BloomTokenizer:()=>Kt,CLIPTokenizer:()=>Os,CamembertTokenizer:()=>ws,CodeGenTokenizer:()=>Ci,CodeLlamaTokenizer:()=>hi,CohereTokenizer:()=>Ys,ConvBertTokenizer:()=>cs,DebertaTokenizer:()=>$A,DebertaV2Tokenizer:()=>PA,DistilBertTokenizer:()=>zs,ElectraTokenizer:()=>Fs,EsmTokenizer:()=>ts,FalconTokenizer:()=>mi,GPT2Tokenizer:()=>xt,GPTNeoXTokenizer:()=>Ur,GemmaTokenizer:()=>ei,Grok1Tokenizer:()=>qa,HerbertTokenizer:()=>qt,LlamaTokenizer:()=>us,M2M100Tokenizer:()=>Ss,MBart50Tokenizer:()=>Hr,MBartTokenizer:()=>Tr,MPNetTokenizer:()=>$s,MarianTokenizer:()=>Ls,MgpstrTokenizer:()=>rs,MobileBertTokenizer:()=>Ia,NllbTokenizer:()=>kr,NougatTokenizer:()=>Ai,PreTrainedTokenizer:()=>bt,Qwen2Tokenizer:()=>ds,RoFormerTokenizer:()=>Za,RobertaTokenizer:()=>gs,SiglipTokenizer:()=>Bs,SpeechT5Tokenizer:()=>wa,SqueezeBertTokenizer:()=>ZA,T5Tokenizer:()=>$r,TokenizerModel:()=>l,VitsTokenizer:()=>ys,Wav2Vec2CTCTokenizer:()=>vs,WhisperTokenizer:()=>Ns,XLMRobertaTokenizer:()=>_s,XLMTokenizer:()=>ks,is_chinese_char:()=>se});var k=d("./src/utils/generic.js"),P=d("./src/utils/core.js"),j=d("./src/utils/hub.js"),O=d("./src/utils/maths.js"),V=d("./src/utils/tensor.js"),q=d("./src/utils/data-structures.js"),R=d("./node_modules/@huggingface/jinja/dist/index.js"),S=d("./src/models/whisper/common_whisper.js");async function X(tA,_){const Be=await Promise.all([(0,j.getModelJSON)(tA,"tokenizer.json",!0,_),(0,j.getModelJSON)(tA,"tokenizer_config.json",!0,_)]);return _.legacy!==null&&(Be[1].legacy=_.legacy),Be}function H(tA,_=!0){if(tA.Regex!==void 0){let Be=tA.Regex.replace(/\\([#&~])/g,"$1");for(const[Oe,Ve]of b)Be=Be.replaceAll(Oe,Ve);return new RegExp(Be,"gu")}if(tA.String!==void 0){const Be=(0,P.escapeRegExp)(tA.String);return new RegExp(_?Be:`(${Be})`,"gu")}return console.warn("Unknown pattern type:",tA),null}function re(tA){return new Map(Object.entries(tA))}function be(tA){const _=tA.dims;switch(_.length){case 1:return tA.tolist();case 2:if(_[0]!==1)throw new Error("Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.");return tA.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${_.length}.`)}}function xe(tA){return tA.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function ye(tA){return tA.replace(/\p{M}/gu,"")}function se(tA){return tA>=19968&&tA<=40959||tA>=13312&&tA<=19903||tA>=131072&&tA<=173791||tA>=173824&&tA<=177983||tA>=177984&&tA<=178207||tA>=178208&&tA<=183983||tA>=63744&&tA<=64255||tA>=194560&&tA<=195103}const ne="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",we=new RegExp(`^[${ne}]+$`,"gu"),g=".,!?…。，、।۔،",b=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"],[` ?[^(\\s|[${g}])]+`,` ?[^\\s${g}]+`]]);class e{constructor(_){this.content=_.content,this.id=_.id,this.single_word=_.single_word??!1,this.lstrip=_.lstrip??!1,this.rstrip=_.rstrip??!1,this.special=_.special??!1,this.normalized=_.normalized??null}}class l extends k.Callable{constructor(_){super(),this.config=_,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=this.config.fuse_unk??!1}static fromConfig(_,...Be){switch(_.type){case"WordPiece":return new M(_);case"Unigram":return new ue(_,...Be);case"BPE":return new Re(_);default:if(_.vocab)return Array.isArray(_.vocab)?new ue(_,...Be):typeof _.vocab=="object"&&_.continuing_subword_prefix&&_.unk_token?new M(_):new Ne(_,...Be);throw new Error(`Unknown TokenizerModel type: ${_.type}`)}}_call(_){return _=this.encode(_),this.fuse_unk&&(_=function(Be,Oe,Ve){const $e=[];let fA=0;for(;fA<Be.length;)if($e.push(Be[fA]),(Oe.get(Be[fA])??Ve)===Ve)for(;++fA<Be.length&&(Oe.get(Be[fA])??Ve)===Ve;)Oe.get($e.at(-1))!==Ve&&($e[$e.length-1]+=Be[fA]);else++fA;return $e}(_,this.tokens_to_ids,this.unk_token_id)),_}encode(_){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(_){return _.map(Be=>this.tokens_to_ids.get(Be)??this.unk_token_id)}convert_ids_to_tokens(_){return _.map(Be=>this.vocab[Be]??this.unk_token)}}class M extends l{constructor(_){super(_),this.tokens_to_ids=re(_.vocab),this.unk_token_id=this.tokens_to_ids.get(_.unk_token),this.unk_token=_.unk_token,this.max_input_chars_per_word=_.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[Be,Oe]of this.tokens_to_ids)this.vocab[Oe]=Be}encode(_){const Be=[];for(const Oe of _){const Ve=[...Oe];if(Ve.length>this.max_input_chars_per_word){Be.push(this.unk_token);continue}let $e=!1,fA=0;const NA=[];for(;fA<Ve.length;){let He=Ve.length,VA=null;for(;fA<He;){let bA=Ve.slice(fA,He).join("");if(fA>0&&(bA=this.config.continuing_subword_prefix+bA),this.tokens_to_ids.has(bA)){VA=bA;break}--He}if(VA===null){$e=!0;break}NA.push(VA),fA=He}$e?Be.push(this.unk_token):Be.push(...NA)}return Be}}class ue extends l{constructor(_,Be){super(_);const Oe=_.vocab.length;this.vocab=new Array(Oe),this.scores=new Array(Oe);for(let Ve=0;Ve<Oe;++Ve)[this.vocab[Ve],this.scores[Ve]]=_.vocab[Ve];this.unk_token_id=_.unk_id,this.unk_token=this.vocab[_.unk_id],this.tokens_to_ids=new Map(this.vocab.map((Ve,$e)=>[Ve,$e])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=Be.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.unk_token=this.vocab[this.unk_token_id],this.minScore=(0,O.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new q.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(_){const Be=_.chars;let Oe=0;for(;Oe<Be.length;){let Ve=!1;const $e=Be.slice(Oe).join(""),fA=this.trie.commonPrefixSearch($e);for(const NA of fA){const He=this.tokens_to_ids.get(NA),VA=this.scores[He],bA=(0,P.len)(NA);_.insert(Oe,bA,VA,He),Ve||bA!==1||(Ve=!0)}Ve||_.insert(Oe,1,this.unk_score,this.unk_token_id),Oe+=1}}tokenize(_){const Be=new q.TokenLattice(_,this.bos_token_id,this.eos_token_id);return this.populateNodes(Be),Be.tokens()}encode(_){const Be=[];for(const Oe of _){const Ve=this.tokenize(Oe);Be.push(...Ve)}return Be}}const D=(()=>{const tA=[...Array.from({length:94},(Ve,$e)=>$e+33),...Array.from({length:12},(Ve,$e)=>$e+161),...Array.from({length:82},(Ve,$e)=>$e+174)],_=tA.slice();let Be=0;for(let Ve=0;Ve<256;++Ve)tA.includes(Ve)||(tA.push(Ve),_.push(256+Be),Be+=1);const Oe=_.map(Ve=>String.fromCharCode(Ve));return Object.fromEntries(tA.map((Ve,$e)=>[Ve,Oe[$e]]))})(),z=(0,P.reverseDictionary)(D);class Re extends l{constructor(_){super(_),this.tokens_to_ids=re(_.vocab),this.unk_token_id=this.tokens_to_ids.get(_.unk_token),this.unk_token=_.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[Oe,Ve]of this.tokens_to_ids)this.vocab[Ve]=Oe;const Be=Array.isArray(_.merges[0]);this.merges=Be?_.merges:_.merges.map(Oe=>Oe.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((Oe,Ve)=>[JSON.stringify(Oe),Ve])),this.end_of_word_suffix=_.end_of_word_suffix,this.continuing_subword_suffix=_.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.cache=new Map}bpe(_){if(_.length===0)return[];const Be=this.cache.get(_);if(Be!==void 0)return Be;const Oe=Array.from(_);this.end_of_word_suffix&&(Oe[Oe.length-1]+=this.end_of_word_suffix);let Ve=[];if(Oe.length>1){const $e=new q.PriorityQueue((He,VA)=>He.score<VA.score);let fA={token:Oe[0],bias:0,prev:null,next:null},NA=fA;for(let He=1;He<Oe.length;++He){const VA={bias:He/Oe.length,token:Oe[He],prev:NA,next:null};NA.next=VA,this._add_node($e,NA),NA=VA}for(;!$e.isEmpty();){const He=$e.pop();if(He.deleted||!He.next||He.next.deleted)continue;if(He.deleted=!0,He.next.deleted=!0,He.prev){const bA={...He.prev};He.prev.deleted=!0,He.prev=bA,bA.prev?bA.prev.next=bA:fA=bA}const VA={token:He.token+He.next.token,bias:He.bias,prev:He.prev,next:He.next.next};VA.prev?(VA.prev.next=VA,this._add_node($e,VA.prev)):fA=VA,VA.next&&(VA.next.prev=VA,this._add_node($e,VA))}for(let He=fA;He!==null;He=He.next)Ve.push(He.token)}else Ve=Oe;if(this.continuing_subword_suffix)for(let $e=0;$e<Ve.length-1;++$e)Ve[$e]+=this.continuing_subword_suffix;return this.cache.set(_,Ve),Ve}_add_node(_,Be){const Oe=this.bpe_ranks.get(JSON.stringify([Be.token,Be.next.token]));Oe!==void 0&&(Be.score=Oe+Be.bias,_.push(Be))}encode(_){const Be=[];for(const Oe of _){if(this.ignore_merges&&this.tokens_to_ids.has(Oe)){Be.push(Oe);continue}const Ve=this.bpe(Oe);for(const $e of Ve)if(this.tokens_to_ids.has($e))Be.push($e);else if(this.byte_fallback){const fA=Array.from(this.text_encoder.encode($e)).map(NA=>`<0x${NA.toString(16).toUpperCase().padStart(2,"0")}>`);fA.every(NA=>this.tokens_to_ids.has(NA))?Be.push(...fA):Be.push(this.unk_token)}else Be.push(this.unk_token)}return Be}}class Ne extends l{constructor(_,Be){super(_),this.tokens_to_ids=re(Be.target_lang?_.vocab[Be.target_lang]:_.vocab),this.bos_token=Be.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=Be.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=Be.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=Be.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(const[Oe,Ve]of this.tokens_to_ids)this.vocab[Ve]=Oe}encode(_){return _}}class Ye extends k.Callable{constructor(_){super(),this.config=_}static fromConfig(_){if(_===null)return null;switch(_.type){case"BertNormalizer":return new kA(_);case"Precompiled":return new Mt(_);case"Sequence":return new Pe(_);case"Replace":return new Je(_);case"NFC":return new AA(_);case"NFKC":return new xA(_);case"NFKD":return new GA(_);case"Strip":return new Ge(_);case"StripAccents":return new Ie(_);case"Lowercase":return new ze(_);case"Prepend":return new Ue(_);default:throw new Error(`Unknown Normalizer type: ${_.type}`)}}normalize(_){throw Error("normalize should be implemented in subclass.")}_call(_){return this.normalize(_)}}class Je extends Ye{normalize(_){const Be=H(this.config.pattern);return Be===null?_:_.replaceAll(Be,this.config.content)}}class AA extends Ye{normalize(_){return _=_.normalize("NFC")}}class xA extends Ye{normalize(_){return _=_.normalize("NFKC")}}class GA extends Ye{normalize(_){return _=_.normalize("NFKD")}}class Ge extends Ye{normalize(_){return this.config.strip_left&&this.config.strip_right?_=_.trim():(this.config.strip_left&&(_=_.trimStart()),this.config.strip_right&&(_=_.trimEnd())),_}}class Ie extends Ye{normalize(_){return _=ye(_)}}class ze extends Ye{normalize(_){return _=_.toLowerCase()}}class Ue extends Ye{normalize(_){return _=this.config.prepend+_}}class Pe extends Ye{constructor(_){super(_),this.normalizers=_.normalizers.map(Be=>Ye.fromConfig(Be))}normalize(_){return this.normalizers.reduce((Be,Oe)=>Oe.normalize(Be),_)}}class kA extends Ye{_tokenize_chinese_chars(_){const Be=[];for(let Oe=0;Oe<_.length;++Oe){const Ve=_[Oe];se(Ve.charCodeAt(0))?(Be.push(" "),Be.push(Ve),Be.push(" ")):Be.push(Ve)}return Be.join("")}stripAccents(_){return _.normalize("NFD").replace(/\p{Mn}/gu,"")}_is_control(_){switch(_){case"	":case`
`:case"\r":return!1;default:return/^\p{Cc}|\p{Cf}|\p{Co}|\p{Cs}$/u.test(_)}}_clean_text(_){const Be=[];for(const Oe of _){const Ve=Oe.charCodeAt(0);Ve===0||Ve===65533||this._is_control(Oe)||(/^\s$/.test(Oe)?Be.push(" "):Be.push(Oe))}return Be.join("")}normalize(_){return this.config.clean_text&&(_=this._clean_text(_)),this.config.handle_chinese_chars&&(_=this._tokenize_chinese_chars(_)),this.config.lowercase?(_=_.toLowerCase(),this.config.strip_accents!==!1&&(_=this.stripAccents(_))):this.config.strip_accents&&(_=this.stripAccents(_)),_}}class aA extends k.Callable{static fromConfig(_){if(_===null)return null;switch(_.type){case"BertPreTokenizer":return new iA(_);case"Sequence":return new Tt(_);case"Whitespace":return new ut(_);case"WhitespaceSplit":return new Ct(_);case"Metaspace":return new QA(_);case"ByteLevel":return new yA(_);case"Split":return new nA(_);case"Punctuation":return new Fe(_);case"Digits":return new Ze(_);case"Replace":return new $t(_);default:throw new Error(`Unknown PreTokenizer type: ${_.type}`)}}pre_tokenize_text(_,Be){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(_,Be){return(Array.isArray(_)?_.map(Oe=>this.pre_tokenize_text(Oe,Be)):this.pre_tokenize_text(_,Be)).flat()}_call(_,Be){return this.pre_tokenize(_,Be)}}class iA extends aA{constructor(_){super(),this.pattern=new RegExp(`[^\\s${ne}]+|[${ne}]`,"gu")}pre_tokenize_text(_,Be){return _.trim().match(this.pattern)||[]}}class yA extends aA{constructor(_){super(),this.config=_,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=D,this.text_encoder=new TextEncoder}pre_tokenize_text(_,Be){return this.add_prefix_space&&!_.startsWith(" ")&&(_=" "+_),(this.use_regex?_.match(this.pattern)||[]:[_]).map(Oe=>Array.from(this.text_encoder.encode(Oe),Ve=>this.byte_encoder[Ve]).join(""))}}class nA extends aA{constructor(_){super(),this.config=_,this.pattern=H(this.config.pattern,this.config.invert)}pre_tokenize_text(_,Be){return this.pattern===null?[]:this.config.invert?_.match(this.pattern)||[]:this.config.behavior?.toLowerCase()==="removed"?_.split(this.pattern).filter(Oe=>Oe):function(Oe,Ve){const $e=[];let fA=0;for(const NA of Oe.matchAll(Ve)){const He=NA[0];fA<NA.index&&$e.push(Oe.slice(fA,NA.index)),He.length>0&&$e.push(He),fA=NA.index+He.length}return fA<Oe.length&&$e.push(Oe.slice(fA)),$e}(_,this.pattern)}}class Fe extends aA{constructor(_){super(),this.config=_,this.pattern=new RegExp(`[^${ne}]+|[${ne}]+`,"gu")}pre_tokenize_text(_,Be){return _.match(this.pattern)||[]}}class Ze extends aA{constructor(_){super(),this.config=_;const Be="[^\\d]+|\\d"+(this.config.individual_digits?"":"+");this.pattern=new RegExp(Be,"gu")}pre_tokenize_text(_,Be){return _.match(this.pattern)||[]}}class mA extends k.Callable{constructor(_){super(),this.config=_}static fromConfig(_){if(_===null)return null;switch(_.type){case"TemplateProcessing":return new pA(_);case"ByteLevel":return new EA(_);case"RobertaProcessing":return new Te(_);case"BertProcessing":return new CA(_);case"Sequence":return new SA(_);default:throw new Error(`Unknown PostProcessor type: ${_.type}`)}}post_process(_,...Be){throw Error("post_process should be implemented in subclass.")}_call(_,...Be){return this.post_process(_,...Be)}}class CA extends mA{constructor(_){super(_),this.cls=_.cls[0],this.sep=_.sep[0]}post_process(_,Be=null,{add_special_tokens:Oe=!0}={}){Oe&&(_=(0,P.mergeArrays)([this.cls],_,[this.sep]));let Ve=new Array(_.length).fill(0);if(Be!==null){const $e=Oe&&this instanceof Te?[this.sep]:[],fA=Oe?[this.sep]:[];_=(0,P.mergeArrays)(_,$e,Be,fA),Ve=(0,P.mergeArrays)(Ve,new Array(Be.length+$e.length+fA.length).fill(1))}return{tokens:_,token_type_ids:Ve}}}class Te extends CA{}class pA extends mA{constructor(_){super(_),this.single=_.single,this.pair=_.pair}post_process(_,Be=null,{add_special_tokens:Oe=!0}={}){const Ve=Be===null?this.single:this.pair;let $e=[],fA=[];for(const NA of Ve)"SpecialToken"in NA?Oe&&($e.push(NA.SpecialToken.id),fA.push(NA.SpecialToken.type_id)):"Sequence"in NA&&(NA.Sequence.id==="A"?($e=(0,P.mergeArrays)($e,_),fA=(0,P.mergeArrays)(fA,new Array(_.length).fill(NA.Sequence.type_id))):NA.Sequence.id==="B"&&($e=(0,P.mergeArrays)($e,Be),fA=(0,P.mergeArrays)(fA,new Array(Be.length).fill(NA.Sequence.type_id))));return{tokens:$e,token_type_ids:fA}}}class EA extends mA{post_process(_,Be=null){return Be&&(_=(0,P.mergeArrays)(_,Be)),{tokens:_}}}class SA extends mA{constructor(_){super(_),this.processors=_.processors.map(Be=>mA.fromConfig(Be))}post_process(_,Be=null,Oe={}){let Ve;for(const $e of this.processors)if($e instanceof EA)_=$e.post_process(_).tokens,Be&&(Be=$e.post_process(Be).tokens);else{const fA=$e.post_process(_,Be,Oe);_=fA.tokens,Ve=fA.token_type_ids}return{tokens:_,token_type_ids:Ve}}}class YA extends k.Callable{constructor(_){super(),this.config=_,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=_.trim_offsets}static fromConfig(_){if(_===null)return null;switch(_.type){case"WordPiece":return new me(_);case"Metaspace":return new TA(_);case"ByteLevel":return new Se(_);case"Replace":return new J(_);case"ByteFallback":return new OA(_);case"Fuse":return new ge(_);case"Strip":return new $(_);case"Sequence":return new dA(_);case"CTC":return new qe(_);case"BPEDecoder":return new oA(_);default:throw new Error(`Unknown Decoder type: ${_.type}`)}}_call(_){return this.decode(_)}decode(_){return this.decode_chain(_).join("")}decode_chain(_){throw Error("`decode_chain` should be implemented in subclass.")}}class J extends YA{decode_chain(_){const Be=H(this.config.pattern);return Be===null?_:_.map(Oe=>Oe.replaceAll(Be,this.config.content))}}class OA extends YA{constructor(_){super(_),this.text_decoder=new TextDecoder}decode_chain(_){const Be=[];let Oe=[];for(const Ve of _){let $e=null;if(Ve.length===6&&Ve.startsWith("<0x")&&Ve.endsWith(">")){const fA=parseInt(Ve.slice(3,5),16);isNaN(fA)||($e=fA)}if($e!==null)Oe.push($e);else{if(Oe.length>0){const fA=this.text_decoder.decode(Uint8Array.from(Oe));Be.push(fA),Oe=[]}Be.push(Ve)}}if(Oe.length>0){const Ve=this.text_decoder.decode(Uint8Array.from(Oe));Be.push(Ve),Oe=[]}return Be}}class ge extends YA{decode_chain(_){return[_.join("")]}}class $ extends YA{constructor(_){super(_),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(_){return _.map(Be=>{let Oe=0;for(let $e=0;$e<this.start&&Be[$e]===this.content;++$e)Oe=$e+1;let Ve=Be.length;for(let $e=0;$e<this.stop;++$e){const fA=Be.length-$e-1;if(Be[fA]!==this.content)break;Ve=fA}return Be.slice(Oe,Ve)})}}class me extends YA{constructor(_){super(_),this.cleanup=_.cleanup}decode_chain(_){return _.map((Be,Oe)=>(Oe!==0&&(Be=Be.startsWith(this.config.prefix)?Be.replace(this.config.prefix,""):" "+Be),this.cleanup&&(Be=xe(Be)),Be))}}class Se extends YA{constructor(_){super(_),this.byte_decoder=z,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(_){const Be=_.join(""),Oe=new Uint8Array([...Be].map(Ve=>this.byte_decoder[Ve]));return this.text_decoder.decode(Oe)}decode_chain(_){const Be=[];let Oe=[];for(const Ve of _)this.added_tokens.find($e=>$e.content===Ve)!==void 0?(Oe.length>0&&(Be.push(this.convert_tokens_to_string(Oe)),Oe=[]),Be.push(Ve)):Oe.push(Ve);return Oe.length>0&&Be.push(this.convert_tokens_to_string(Oe)),Be}}class qe extends YA{constructor(_){super(_),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(_){if(_.length===0)return"";const Be=[_[0]];for(let Ve=1;Ve<_.length;++Ve)_[Ve]!==Be.at(-1)&&Be.push(_[Ve]);let Oe=Be.filter(Ve=>Ve!==this.pad_token).join("");return this.cleanup&&(Oe=xe(Oe).replaceAll(this.word_delimiter_token," ").trim()),Oe}decode_chain(_){return[this.convert_tokens_to_string(_)]}}class dA extends YA{constructor(_){super(_),this.decoders=_.decoders.map(Be=>YA.fromConfig(Be))}decode_chain(_){return this.decoders.reduce((Be,Oe)=>Oe.decode_chain(Be),_)}}class oA extends YA{constructor(_){super(_),this.suffix=this.config.suffix}decode_chain(_){return _.map((Be,Oe)=>Be.replaceAll(this.suffix,Oe===_.length-1?"":" "))}}class BA extends YA{decode_chain(_){let Be="";for(let Oe=1;Oe<_.length;Oe+=2)Be+=_[Oe];return[Be]}}class QA extends aA{constructor(_){super(),this.addPrefixSpace=_.add_prefix_space,this.replacement=_.replacement,this.strRep=_.str_rep||this.replacement,this.prepend_scheme=_.prepend_scheme??"always"}pre_tokenize_text(_,{section_index:Be}={}){let Oe=_.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!Oe.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&Be===0)&&(Oe=this.strRep+Oe),[Oe]}}class TA extends YA{constructor(_){super(_),this.addPrefixSpace=_.add_prefix_space,this.replacement=_.replacement}decode_chain(_){const Be=[];for(let Oe=0;Oe<_.length;++Oe){let Ve=_[Oe].replaceAll(this.replacement," ");this.addPrefixSpace&&Oe==0&&Ve.startsWith(" ")&&(Ve=Ve.substring(1)),Be.push(Ve)}return Be}}class Mt extends Ye{constructor(_){super(_),this.charsmap=_.precompiled_charsmap}normalize(_){return(_=(_=_.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,"")).replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm," ")).includes("～")?_=_.split("～").map(Oe=>Oe.normalize("NFKC")).join("～"):_=_.normalize("NFKC"),_}}class Tt extends aA{constructor(_){super(),this.tokenizers=_.pretokenizers.map(Be=>aA.fromConfig(Be))}pre_tokenize_text(_,Be){return this.tokenizers.reduce((Oe,Ve)=>Ve.pre_tokenize(Oe,Be),[_])}}class ut extends aA{constructor(_){super()}pre_tokenize_text(_,Be){return _.match(/\w+|[^\w\s]+/g)||[]}}class Ct extends aA{constructor(_){super()}pre_tokenize_text(_,Be){return function(Oe){return Oe.match(/\S+/g)||[]}(_)}}class $t extends aA{constructor(_){super(),this.config=_,this.pattern=H(this.config.pattern),this.content=this.config.content}pre_tokenize_text(_,Be){return this.pattern===null?[_]:[_.replaceAll(this.pattern,this.config.content)]}}const Qr=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function tr(tA,_,Be,Oe){for(const Ve of Object.keys(tA)){const $e=_-tA[Ve].length,fA=Be(Ve),NA=new Array($e).fill(fA);tA[Ve]=Oe==="right"?(0,P.mergeArrays)(tA[Ve],NA):(0,P.mergeArrays)(NA,tA[Ve])}}function da(tA,_){for(const Be of Object.keys(tA))tA[Be].length=_}class bt extends k.Callable{return_token_type_ids=!1;padding_side="right";constructor(_,Be){super(),this._tokenizer_config=Be,this.normalizer=Ye.fromConfig(_.normalizer),this.pre_tokenizer=aA.fromConfig(_.pre_tokenizer),this.model=l.fromConfig(_.model,Be),this.post_processor=mA.fromConfig(_.post_processor),this.decoder=YA.fromConfig(_.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const Oe of _.added_tokens){const Ve=new e(Oe);this.added_tokens.push(Ve),this.model.tokens_to_ids.set(Ve.content,Ve.id),this.model.vocab[Ve.id]=Ve.content,Ve.special&&(this.special_tokens.push(Ve.content),this.all_special_ids.push(Ve.id))}if(this.additional_special_tokens=Be.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.slice().sort((Oe,Ve)=>Ve.content.length-Oe.content.length).map(Oe=>`${Oe.lstrip?"\\s*":""}(${(0,P.escapeRegExp)(Oe.content)})${Oe.rstrip?"\\s*":""}`).join("|")):null,this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.bos_token=this.getToken("bos_token"),this.bos_token_id=this.model.tokens_to_ids.get(this.bos_token),this.eos_token=this.getToken("eos_token"),this.eos_token_id=this.model.tokens_to_ids.get(this.eos_token),this.model_max_length=Be.model_max_length,this.remove_space=Be.remove_space,this.clean_up_tokenization_spaces=Be.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=Be.do_lowercase_and_remove_accent??!1,Be.padding_side&&(this.padding_side=Be.padding_side),this.legacy=!1,this.chat_template=Be.chat_template??null,Array.isArray(this.chat_template)){const Oe=Object.create(null);for(const{name:Ve,template:$e}of this.chat_template){if(typeof Ve!="string"||typeof $e!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');Oe[Ve]=$e}this.chat_template=Oe}this._compiled_template_cache=new Map}getToken(..._){for(const Be of _){const Oe=this._tokenizer_config[Be];if(Oe){if(typeof Oe=="object"){if(Oe.__type==="AddedToken")return Oe.content;throw Error(`Unknown token: ${Oe}`)}return Oe}}return null}static async from_pretrained(_,{progress_callback:Be=null,config:Oe=null,cache_dir:Ve=null,local_files_only:$e=!1,revision:fA="main",legacy:NA=null}={}){return new this(...await X(_,{progress_callback:Be,config:Oe,cache_dir:Ve,local_files_only:$e,revision:fA,legacy:NA}))}_call(_,{text_pair:Be=null,add_special_tokens:Oe=!0,padding:Ve=!1,truncation:$e=null,max_length:fA=null,return_tensor:NA=!0,return_token_type_ids:He=null}={}){const VA=Array.isArray(_);let bA;if(VA){if(_.length===0)throw Error("text array must be non-empty");if(Be!==null){if(!Array.isArray(Be))throw Error("text_pair must also be an array");if(_.length!==Be.length)throw Error("text and text_pair must have the same length");bA=_.map((Vt,cr)=>this._encode_plus(Vt,{text_pair:Be[cr],add_special_tokens:Oe,return_token_type_ids:He}))}else bA=_.map(Vt=>this._encode_plus(Vt,{add_special_tokens:Oe,return_token_type_ids:He}))}else{if(_==null)throw Error("text may not be null or undefined");if(Array.isArray(Be))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");bA=[this._encode_plus(_,{text_pair:Be,add_special_tokens:Oe,return_token_type_ids:He})]}if(fA===null?fA=Ve==="max_length"?this.model_max_length:(0,O.max)(bA.map(Vt=>Vt.input_ids.length))[0]:$e||console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=true` to explicitly truncate examples to max length."),fA=Math.min(fA,this.model_max_length??1/0),Ve||$e)for(let Vt=0;Vt<bA.length;++Vt)bA[Vt].input_ids.length!==fA&&(bA[Vt].input_ids.length>fA?$e&&da(bA[Vt],fA):Ve&&tr(bA[Vt],fA,cr=>cr==="input_ids"?this.pad_token_id:0,this.padding_side));const Yt={};if(NA){if((!Ve||!$e)&&bA.some(cr=>{for(const sr of Object.keys(cr))if(cr[sr].length!==bA[0][sr]?.length)return!0;return!1}))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");const Vt=[bA.length,bA[0].input_ids.length];for(const cr of Object.keys(bA[0]))Yt[cr]=new V.Tensor("int64",BigInt64Array.from(bA.flatMap(sr=>sr[cr]).map(BigInt)),Vt)}else{for(const Vt of Object.keys(bA[0]))Yt[Vt]=bA.map(cr=>cr[Vt]);if(!VA)for(const Vt of Object.keys(Yt))Yt[Vt]=Yt[Vt][0]}return Yt}_encode_text(_){return _===null?null:(this.added_tokens_regex?_.split(this.added_tokens_regex).filter(Ve=>Ve):[_]).map((Ve,$e)=>{if(this.added_tokens.find(NA=>NA.content===Ve)!==void 0)return Ve;{if(this.remove_space===!0&&(Ve=Ve.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(Ve=function(He){return ye(He.toLowerCase())}(Ve)),this.normalizer!==null&&(Ve=this.normalizer(Ve)),Ve.length===0)return[];const NA=this.pre_tokenizer!==null?this.pre_tokenizer(Ve,{section_index:$e}):[Ve];return this.model(NA)}}).flat()}_encode_plus(_,{text_pair:Be=null,add_special_tokens:Oe=!0,return_token_type_ids:Ve=null}={}){const{tokens:$e,token_type_ids:fA}=this._tokenize_helper(_,{pair:Be,add_special_tokens:Oe}),NA=this.model.convert_tokens_to_ids($e),He={input_ids:NA,attention_mask:new Array(NA.length).fill(1)};return(Ve??this.return_token_type_ids)&&fA&&(He.token_type_ids=fA),He}_tokenize_helper(_,{pair:Be=null,add_special_tokens:Oe=!1}={}){const Ve=this._encode_text(_),$e=this._encode_text(Be);return this.post_processor?this.post_processor(Ve,$e,{add_special_tokens:Oe}):{tokens:(0,P.mergeArrays)(Ve??[],$e??[])}}tokenize(_,{pair:Be=null,add_special_tokens:Oe=!1}={}){return this._tokenize_helper(_,{pair:Be,add_special_tokens:Oe}).tokens}encode(_,{text_pair:Be=null,add_special_tokens:Oe=!0,return_token_type_ids:Ve=null}={}){return this._encode_plus(_,{text_pair:Be,add_special_tokens:Oe,return_token_type_ids:Ve}).input_ids}batch_decode(_,Be={}){return _ instanceof V.Tensor&&(_=_.tolist()),_.map(Oe=>this.decode(Oe,Be))}decode(_,Be={}){if(_ instanceof V.Tensor&&(_=be(_)),!Array.isArray(_)||_.length===0||!(0,P.isIntegralNumber)(_[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(_,Be)}decode_single(_,{skip_special_tokens:Be=!1,clean_up_tokenization_spaces:Oe=null}){let Ve=this.model.convert_ids_to_tokens(_);Be&&(Ve=Ve.filter(fA=>!this.special_tokens.includes(fA)));let $e=this.decoder?this.decoder(Ve):Ve.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&($e=$e.replaceAll(this.decoder.end_of_word_suffix," "),Be&&($e=$e.trim())),(Oe??this.clean_up_tokenization_spaces)&&($e=xe($e)),$e}get_chat_template({chat_template:_=null,tools:Be=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const Oe=this.chat_template;if(_!==null&&Object.hasOwn(Oe,_))_=Oe[_];else if(_===null)if(Be!==null&&"tool_use"in Oe)_=Oe.tool_use;else{if(!("default"in Oe))throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(Oe).sort()}.`);_=Oe.default}}else if(_===null){if(!this.chat_template)throw Error("Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating");_=this.chat_template}return _}apply_chat_template(_,{tools:Be=null,documents:Oe=null,chat_template:Ve=null,add_generation_prompt:$e=!1,tokenize:fA=!0,padding:NA=!1,truncation:He=!1,max_length:VA=null,return_tensor:bA=!0,return_dict:Yt=!1,tokenizer_kwargs:Vt={},...cr}={}){if(typeof(Ve=this.get_chat_template({chat_template:Ve,tools:Be}))!="string")throw Error("chat_template must be a string, but got "+typeof Ve);let sr=this._compiled_template_cache.get(Ve);sr===void 0&&(sr=new R.Template(Ve),this._compiled_template_cache.set(Ve,sr));const jt=Object.create(null);for(const KA of Qr){const Jt=this.getToken(KA);Jt&&(jt[KA]=Jt)}const jA=sr.render({messages:_,add_generation_prompt:$e,tools:Be,documents:Oe,...jt,...cr});if(fA){const KA=this._call(jA,{add_special_tokens:!1,padding:NA,truncation:He,max_length:VA,return_tensor:bA,...Vt});return Yt?KA:KA.input_ids}return jA}}class Ka extends bt{return_token_type_ids=!0}class Oa extends bt{return_token_type_ids=!0}class Ia extends bt{return_token_type_ids=!0}class ZA extends bt{return_token_type_ids=!0}class $A extends bt{return_token_type_ids=!0}class PA extends bt{return_token_type_ids=!0}class qt extends bt{return_token_type_ids=!0}class cs extends bt{return_token_type_ids=!0}class Za extends bt{return_token_type_ids=!0}class zs extends bt{}class ws extends bt{}class ks extends bt{return_token_type_ids=!0;constructor(_,Be){super(_,Be),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class Fs extends bt{return_token_type_ids=!0}class $r extends bt{}class xt extends bt{}class Ea extends bt{}class Tr extends bt{constructor(_,Be){super(_,Be),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(Oe=>this.languageRegex.test(Oe)),this.lang_to_token=Oe=>Oe}_build_translation_inputs(_,Be,Oe){return xs(this,_,Be,Oe)}}class Hr extends Tr{}class gs extends bt{}class Kt extends bt{}const Xa="▁";class us extends bt{padding_side="left";constructor(_,Be){super(_,Be),this.legacy=Be.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new QA({replacement:Xa,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(_){if(_===null)return null;if(this.legacy||_.length===0)return super._encode_text(_);let Be=super._encode_text(Xa+_.replaceAll(Xa," "));return Be.length>1&&Be[0]===Xa&&this.special_tokens.includes(Be[1])&&(Be=Be.slice(1)),Be}}class hi extends bt{}class _s extends bt{}class $s extends bt{}class mi extends bt{}class Ur extends bt{}class ts extends bt{}class ds extends bt{}class ei extends bt{}class qa extends bt{}function xs(tA,_,Be,Oe){if(!("language_codes"in tA)||!Array.isArray(tA.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in tA&&tA.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in tA)||typeof tA.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const Ve=Oe.src_lang,$e=Oe.tgt_lang;if(!tA.language_codes.includes($e))throw new Error(`Target language code "${$e}" is not valid. Must be one of: {${tA.language_codes.join(", ")}}`);if(Ve!==void 0){if(!tA.language_codes.includes(Ve))throw new Error(`Source language code "${Ve}" is not valid. Must be one of: {${tA.language_codes.join(", ")}}`);for(const fA of tA.post_processor.config.single)if("SpecialToken"in fA&&tA.languageRegex.test(fA.SpecialToken.id)){fA.SpecialToken.id=tA.lang_to_token(Ve);break}}return Oe.forced_bos_token_id=tA.model.convert_tokens_to_ids([tA.lang_to_token($e)])[0],tA._call(_,Be)}class kr extends bt{constructor(_,Be){super(_,Be),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(Oe=>this.languageRegex.test(Oe)),this.lang_to_token=Oe=>Oe}_build_translation_inputs(_,Be,Oe){return xs(this,_,Be,Oe)}}class Ss extends bt{constructor(_,Be){super(_,Be),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(Oe=>this.languageRegex.test(Oe)).map(Oe=>Oe.slice(2,-2)),this.lang_to_token=Oe=>`__${Oe}__`}_build_translation_inputs(_,Be,Oe){return xs(this,_,Be,Oe)}}class Ns extends bt{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(_,{return_timestamps:Be=!1,return_language:Oe=!1,time_precision:Ve=null,force_full_sequences:$e=!0}={}){if(Ve===null)throw Error("Must specify time_precision");let fA=null;const NA=Be==="word";function He(){return{language:fA,timestamp:[null,null],text:""}}const VA=[];let bA=He(),Yt=0;const Vt=this.timestamp_begin,cr=Vt+1500;let sr=[],jt=[],jA=!1,KA=null;const Jt=new Set(this.all_special_ids);for(const ir of _){const rt=ir.tokens,dr=NA?ir.token_timestamps:null;let gA=null,JA=Vt;if("stride"in ir){const[Rr,Pr,Cr]=ir.stride;if(Yt-=Pr,KA=Rr-Cr,Pr&&(JA=Pr/Ve+Vt),Cr)for(let Ut=rt.length-1;Ut>=0;--Ut){const xr=Number(rt[Ut]);if(xr>=Vt){if(gA!==null&&(xr-Vt)*Ve<KA)break;gA=xr}}}let pa=[],Nr=[];for(let Rr=0;Rr<rt.length;++Rr){const Pr=Number(rt[Rr]);if(Jt.has(Pr)){const Cr=this.decode([Pr]),Ut=S.WHISPER_LANGUAGE_MAPPING.get(Cr.slice(2,-2));if(Ut!==void 0){if(fA!==null&&Ut!==fA&&!Be){sr.push(pa);const xr=this.findLongestCommonSequence(sr)[0],ti=this.decode(xr);bA.text=ti,VA.push(bA),sr=[],pa=[],bA=He()}fA=bA.language=Ut}}else if(Pr>=Vt&&Pr<=cr){const Cr=(Pr-Vt)*Ve+Yt,Ut=(0,O.round)(Cr,2);if(gA!==null&&Pr>=gA)jA=!0;else if(jA||sr.length>0&&Pr<JA)jA=!1;else if(bA.timestamp[0]===null)bA.timestamp[0]=Ut;else if(Ut!==bA.timestamp[0]){bA.timestamp[1]=Ut,sr.push(pa),NA&&jt.push(Nr);const[xr,ti]=this.findLongestCommonSequence(sr,jt),Ya=this.decode(xr);bA.text=Ya,NA&&(bA.words=this.collateWordTimestamps(xr,ti,fA)),VA.push(bA),sr=[],pa=[],jt=[],Nr=[],bA=He()}}else if(pa.push(Pr),NA){let Cr,Ut=(0,O.round)(dr[Rr]+Yt,2);if(Rr+1<dr.length){Cr=(0,O.round)(dr[Rr+1]+Yt,2);const xr=this.decode([Pr]);we.test(xr)&&(Cr=(0,O.round)(Math.min(Ut+Ve,Cr),2))}else Cr=null;Nr.push([Ut,Cr])}}if("stride"in ir){const[Rr,Pr,Cr]=ir.stride;Yt+=Rr-Cr}pa.length>0?(sr.push(pa),NA&&jt.push(Nr)):sr.every(Rr=>Rr.length===0)&&(bA=He(),sr=[],pa=[],jt=[],Nr=[])}if(sr.length>0){if($e&&Be)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[ir,rt]=this.findLongestCommonSequence(sr,jt),dr=this.decode(ir);bA.text=dr,NA&&(bA.words=this.collateWordTimestamps(ir,rt,fA)),VA.push(bA)}let La=Object.create(null);const fa=VA.map(ir=>ir.text).join("");if(Be||Oe){for(let ir=0;ir<VA.length;++ir){const rt=VA[ir];Be||delete rt.timestamp,Oe||delete rt.language}if(NA){const ir=[];for(const rt of VA)for(const dr of rt.words)ir.push(dr);La={chunks:ir}}else La={chunks:VA}}return[fa,La]}findLongestCommonSequence(_,Be=null){let Oe=_[0],Ve=Oe.length,$e=[];const fA=Array.isArray(Be)&&Be.length>0;let NA=fA?[]:null,He=fA?Be[0]:null;for(let VA=1;VA<_.length;++VA){const bA=_[VA];let Yt=0,Vt=[Ve,Ve,0,0];const cr=bA.length;for(let fa=1;fa<Ve+cr;++fa){const ir=Math.max(0,Ve-fa),rt=Math.min(Ve,Ve+cr-fa),dr=Oe.slice(ir,rt),gA=Math.max(0,fa-Ve),JA=Math.min(cr,fa),pa=bA.slice(gA,JA);if(dr.length!==pa.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");let Nr;Nr=fA?dr.filter((Pr,Cr)=>Pr===pa[Cr]&&He[ir+Cr]<=Be[VA][gA+Cr]).length:dr.filter((Pr,Cr)=>Pr===pa[Cr]).length;const Rr=Nr/fa+fa/1e4;Nr>1&&Rr>Yt&&(Yt=Rr,Vt=[ir,rt,gA,JA])}const[sr,jt,jA,KA]=Vt,Jt=Math.floor((jt+sr)/2),La=Math.floor((KA+jA)/2);$e.push(...Oe.slice(0,Jt)),Oe=bA.slice(La),Ve=Oe.length,fA&&(NA.push(...He.slice(0,Jt)),He=Be[VA].slice(La))}return $e.push(...Oe),fA?(NA.push(...He),[$e,NA]):[$e,[]]}collateWordTimestamps(_,Be,Oe){const[Ve,$e,fA]=this.combineTokensIntoWords(_,Oe),NA=[];for(let He=0;He<Ve.length;++He){const VA=fA[He];NA.push({text:Ve[He],timestamp:[Be[VA.at(0)][0],Be[VA.at(-1)][1]]})}return NA}combineTokensIntoWords(_,Be,Oe=`"'“¡¿([{-`,Ve=`"'.。,，!！?？:：”)]}、`){let $e,fA,NA;return["chinese","japanese","thai","lao","myanmar"].includes(Be=Be??"english")?[$e,fA,NA]=this.splitTokensOnUnicode(_):[$e,fA,NA]=this.splitTokensOnSpaces(_),this.mergePunctuations($e,fA,NA,Oe,Ve)}decode(_,Be){let Oe;return Be?.decode_with_timestamps?(_ instanceof V.Tensor&&(_=be(_)),Oe=this.decodeWithTimestamps(_,Be)):Oe=super.decode(_,Be),Oe}decodeWithTimestamps(_,Be){const Oe=Be?.time_precision??.02,Ve=Array.from(this.all_special_ids).at(-1)+1;let $e=[[]];for(let fA of _)if(fA=Number(fA),fA>=Ve){const NA=((fA-Ve)*Oe).toFixed(2);$e.push(`<|${NA}|>`),$e.push([])}else $e[$e.length-1].push(fA);return $e=$e.map(fA=>typeof fA=="string"?fA:super.decode(fA,Be)),$e.join("")}splitTokensOnUnicode(_){const Be=this.decode(_,{decode_with_timestamps:!0}),Oe=[],Ve=[],$e=[];let fA=[],NA=[],He=0;for(let VA=0;VA<_.length;++VA){const bA=_[VA];fA.push(bA),NA.push(VA);const Yt=this.decode(fA,{decode_with_timestamps:!0});Yt.includes("�")&&Be[He+Yt.indexOf("�")]!=="�"||(Oe.push(Yt),Ve.push(fA),$e.push(NA),fA=[],NA=[],He+=Yt.length)}return[Oe,Ve,$e]}splitTokensOnSpaces(_){const[Be,Oe,Ve]=this.splitTokensOnUnicode(_),$e=[],fA=[],NA=[],He=new RegExp(`^[${ne}]$`,"gu");for(let VA=0;VA<Be.length;++VA){const bA=Be[VA],Yt=Oe[VA],Vt=Ve[VA],cr=Yt[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),sr=bA.startsWith(" "),jt=bA.trim(),jA=He.test(jt);if(cr||sr||jA||$e.length===0)$e.push(bA),fA.push(Yt),NA.push(Vt);else{const KA=$e.length-1;$e[KA]+=bA,fA[KA].push(...Yt),NA[KA].push(...Vt)}}return[$e,fA,NA]}mergePunctuations(_,Be,Oe,Ve,$e){const fA=structuredClone(_),NA=structuredClone(Be),He=structuredClone(Oe);let VA=fA.length-2,bA=fA.length-1;for(;VA>=0;)fA[VA].startsWith(" ")&&Ve.includes(fA[VA].trim())?(fA[bA]=fA[VA]+fA[bA],NA[bA]=(0,P.mergeArrays)(NA[VA],NA[bA]),He[bA]=(0,P.mergeArrays)(He[VA],He[bA]),fA[VA]="",NA[VA]=[],He[VA]=[]):bA=VA,--VA;for(VA=0,bA=1;bA<fA.length;)!fA[VA].endsWith(" ")&&$e.includes(fA[bA])?(fA[VA]+=fA[bA],NA[VA]=(0,P.mergeArrays)(NA[VA],NA[bA]),He[VA]=(0,P.mergeArrays)(He[VA],He[bA]),fA[bA]="",NA[bA]=[],He[bA]=[]):VA=bA,++bA;return[fA.filter(Yt=>Yt),NA.filter(Yt=>Yt.length>0),He.filter(Yt=>Yt.length>0)]}}class Ci extends bt{}class Os extends bt{}class Bs extends bt{}class Ls extends bt{constructor(_,Be){super(_,Be),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(Oe=>this.languageRegex.test(Oe)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(_){if(_===null)return null;const[Be,...Oe]=_.trim().split(this.languageRegex);if(Oe.length===0)return super._encode_text(Be);if(Oe.length===2){const[Ve,$e]=Oe;return this.supported_language_codes.includes(Ve)||console.warn(`Unsupported language code "${Ve}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),(0,P.mergeArrays)([Ve],super._encode_text($e))}}}class vs extends bt{}class wt extends bt{}class Pt extends bt{}class wa extends bt{}class Ai extends bt{}class ys extends bt{constructor(_,Be){super(_,Be),this.decoder=new BA({})}}class Ys extends bt{}class rs extends bt{}class Hs{static TOKENIZER_CLASS_MAPPING={T5Tokenizer:$r,DistilBertTokenizer:zs,CamembertTokenizer:ws,DebertaTokenizer:$A,DebertaV2Tokenizer:PA,BertTokenizer:Ka,HerbertTokenizer:qt,ConvBertTokenizer:cs,RoFormerTokenizer:Za,XLMTokenizer:ks,ElectraTokenizer:Fs,MobileBertTokenizer:Ia,SqueezeBertTokenizer:ZA,AlbertTokenizer:Oa,GPT2Tokenizer:xt,BartTokenizer:Ea,MBartTokenizer:Tr,MBart50Tokenizer:Hr,RobertaTokenizer:gs,WhisperTokenizer:Ns,CodeGenTokenizer:Ci,CLIPTokenizer:Os,SiglipTokenizer:Bs,MarianTokenizer:Ls,BloomTokenizer:Kt,NllbTokenizer:kr,M2M100Tokenizer:Ss,LlamaTokenizer:us,CodeLlamaTokenizer:hi,XLMRobertaTokenizer:_s,MPNetTokenizer:$s,FalconTokenizer:mi,GPTNeoXTokenizer:Ur,EsmTokenizer:ts,Wav2Vec2CTCTokenizer:vs,BlenderbotTokenizer:wt,BlenderbotSmallTokenizer:Pt,SpeechT5Tokenizer:wa,NougatTokenizer:Ai,VitsTokenizer:ys,Qwen2Tokenizer:ds,GemmaTokenizer:ei,Grok1Tokenizer:qa,CohereTokenizer:Ys,MgpstrTokenizer:rs,PreTrainedTokenizer:bt};static async from_pretrained(_,{progress_callback:Be=null,config:Oe=null,cache_dir:Ve=null,local_files_only:$e=!1,revision:fA="main",legacy:NA=null}={}){const[He,VA]=await X(_,{progress_callback:Be,config:Oe,cache_dir:Ve,local_files_only:$e,revision:fA,legacy:NA}),bA=VA.tokenizer_class?.replace(/Fast$/,"")??"PreTrainedTokenizer";let Yt=this.TOKENIZER_CLASS_MAPPING[bA];return Yt||(console.warn(`Unknown tokenizer class "${bA}", attempting to construct from base class.`),Yt=bt),new Yt(He,VA)}}},"./src/utils/audio.js":(x,C,d)=>{d.r(C),d.d(C,{RawAudio:()=>e,hamming:()=>H,hanning:()=>X,mel_filter_bank:()=>se,read_audio:()=>R,spectrogram:()=>we,window_function:()=>g});var k=d("./src/utils/hub.js"),P=d("./src/utils/maths.js"),j=d("./src/utils/core.js"),O=d("./src/env.js"),V=d("?7a2c"),q=d("./src/utils/tensor.js");async function R(l,M){if(typeof AudioContext>"u")throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. Instead, audio data should be passed directly to the pipeline/processor. For more information and some example code, see https://huggingface.co/docs/transformers.js/guides/node-audio-processing.");const ue=await(await(0,k.getFile)(l)).arrayBuffer(),D=new AudioContext({sampleRate:M});M===void 0&&console.warn(`No sampling rate provided, using default of ${D.sampleRate}Hz.`);const z=await D.decodeAudioData(ue);let Re;if(z.numberOfChannels===2){const Ne=Math.sqrt(2),Ye=z.getChannelData(0),Je=z.getChannelData(1);Re=new Float32Array(Ye.length);for(let AA=0;AA<z.length;++AA)Re[AA]=Ne*(Ye[AA]+Je[AA])/2}else Re=z.getChannelData(0);return Re}function S(l,M){if(l<1)return new Float64Array;if(l===1)return new Float64Array([1]);const ue=1-M,D=2*Math.PI/(l-1),z=new Float64Array(l);for(let Re=0;Re<l;++Re)z[Re]=M-ue*Math.cos(Re*D);return z}function X(l){return S(l,.5)}function H(l){return S(l,.54)}const re={htk:l=>2595*Math.log10(1+l/700),kaldi:l=>1127*Math.log(1+l/700),slaney:(l,M=1e3,ue=15,D=27/Math.log(6.4))=>l>=M?ue+Math.log(l/M)*D:3*l/200};function be(l,M="htk"){const ue=re[M];if(!ue)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof l=="number"?ue(l):l.map(D=>ue(D))}const xe={htk:l=>700*(10**(l/2595)-1),kaldi:l=>700*(Math.exp(l/1127)-1),slaney:(l,M=1e3,ue=15,D=Math.log(6.4)/27)=>l>=ue?M*Math.exp(D*(l-ue)):200*l/3};function ye(l,M,ue){const D=(M-l)/(ue-1);return Float64Array.from({length:ue},(z,Re)=>l+D*Re)}function se(l,M,ue,D,z,Re=null,Ne="htk",Ye=!1){if(Re!==null&&Re!=="slaney")throw new Error('norm must be one of null or "slaney"');const Je=ye(be(ue,Ne),be(D,Ne),M+2);let AA,xA=function(Ge,Ie="htk"){const ze=xe[Ie];if(!ze)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof Ge=="number"?ze(Ge):Ge.map(Ue=>ze(Ue))}(Je,Ne);if(Ye){const Ge=z/(2*l);AA=be(Float64Array.from({length:l},(Ie,ze)=>ze*Ge),Ne),xA=Je}else AA=ye(0,Math.floor(z/2),l);const GA=function(Ge,Ie){const ze=Float64Array.from({length:Ie.length-1},(aA,iA)=>Ie[iA+1]-Ie[iA]),Ue=Array.from({length:Ge.length},()=>new Array(Ie.length));for(let aA=0;aA<Ge.length;++aA){const iA=Ue[aA];for(let yA=0;yA<Ie.length;++yA)iA[yA]=Ie[yA]-Ge[aA]}const Pe=Ie.length-2,kA=Array.from({length:Pe},()=>new Array(Ge.length));for(let aA=0;aA<Ge.length;++aA){const iA=Ue[aA];for(let yA=0;yA<Pe;++yA){const nA=-iA[yA]/ze[yA],Fe=iA[yA+2]/ze[yA+1];kA[yA][aA]=Math.max(0,Math.min(nA,Fe))}}return kA}(AA,xA);if(Re!==null&&Re==="slaney")for(let Ge=0;Ge<M;++Ge){const Ie=GA[Ge],ze=2/(xA[Ge+2]-xA[Ge]);for(let Ue=0;Ue<l;++Ue)Ie[Ue]*=ze}return GA}function ne(l,M,ue,D,z){if(ue<=0)throw new Error("reference must be greater than zero");if(D<=0)throw new Error("min_value must be greater than zero");ue=Math.max(D,ue);const Re=Math.log10(ue);for(let Ne=0;Ne<l.length;++Ne)l[Ne]=M*Math.log10(Math.max(D,l[Ne])-Re);if(z!==null){if(z<=0)throw new Error("db_range must be greater than zero");const Ne=(0,P.max)(l)[0]-z;for(let Ye=0;Ye<l.length;++Ye)l[Ye]=Math.max(l[Ye],Ne)}return l}async function we(l,M,ue,D,{fft_length:z=null,power:Re=1,center:Ne=!0,pad_mode:Ye="reflect",onesided:Je=!0,preemphasis:AA=null,mel_filters:xA=null,mel_floor:GA=1e-10,log_mel:Ge=null,reference:Ie=1,min_value:ze=1e-10,db_range:Ue=null,remove_dc_offset:Pe=null,min_num_frames:kA=null,max_num_frames:aA=null,do_pad:iA=!0,transpose:yA=!1}={}){const nA=M.length;if(z===null&&(z=ue),ue>z)throw Error(`frame_length (${ue}) may not be larger than fft_length (${z})`);if(nA!==ue)throw new Error(`Length of the window (${nA}) must equal frame_length (${ue})`);if(D<=0)throw new Error("hop_length must be greater than zero");if(Re===null&&xA!==null)throw new Error("You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram. Specify `power` to fix this issue.");if(Ne){if(Ye!=="reflect")throw new Error(`pad_mode="${Ye}" not implemented yet.`);const ge=Math.floor((z-1)/2)+1;l=function($,me,Se){const qe=new $.constructor($.length+me+Se),dA=$.length-1;for(let oA=0;oA<$.length;++oA)qe[me+oA]=$[oA];for(let oA=1;oA<=me;++oA)qe[me-oA]=$[(0,j.calculateReflectOffset)(oA,dA)];for(let oA=1;oA<=Se;++oA)qe[dA+me+oA]=$[(0,j.calculateReflectOffset)(dA-oA,dA)];return qe}(l,ge,ge)}let Fe=Math.floor(1+Math.floor((l.length-ue)/D));kA!==null&&Fe<kA&&(Fe=kA);const Ze=Je?Math.floor(z/2)+1:z;let mA=Fe,CA=Fe;aA!==null&&(aA>Fe?iA&&(CA=aA):CA=mA=aA);const Te=new P.FFT(z),pA=new Float64Array(z),EA=new Float64Array(Te.outputBufferSize),SA=new Float32Array(Ze*CA);for(let ge=0;ge<mA;++ge){const $=ge*D,me=Math.min(l.length-$,ue);me!==ue&&pA.fill(0,0,ue);for(let Se=0;Se<me;++Se)pA[Se]=l[$+Se];if(Pe){let Se=0;for(let dA=0;dA<me;++dA)Se+=pA[dA];const qe=Se/me;for(let dA=0;dA<me;++dA)pA[dA]-=qe}if(AA!==null){for(let Se=me-1;Se>=1;--Se)pA[Se]-=AA*pA[Se-1];pA[0]*=1-AA}for(let Se=0;Se<M.length;++Se)pA[Se]*=M[Se];Te.realTransform(EA,pA);for(let Se=0;Se<Ze;++Se){const qe=Se<<1;SA[Se*CA+ge]=EA[qe]**2+EA[qe+1]**2}}if(Re!==null&&Re!==2){const ge=2/Re;for(let $=0;$<SA.length;++$)SA[$]**=ge}const YA=xA.length;let J=await(0,q.matmul)(new q.Tensor("float32",xA.flat(),[YA,Ze]),new q.Tensor("float32",SA,[Ze,CA]));yA&&(J=J.transpose(1,0));const OA=J.data;for(let ge=0;ge<OA.length;++ge)OA[ge]=Math.max(GA,OA[ge]);if(Re!==null&&Ge!==null){const ge=Math.min(OA.length,mA*YA);switch(Ge){case"log":for(let $=0;$<ge;++$)OA[$]=Math.log(OA[$]);break;case"log10":for(let $=0;$<ge;++$)OA[$]=Math.log10(OA[$]);break;case"dB":if(Re===1)(function($,me=1,Se=1e-5,qe=null){ne($,20,me,Se,qe)})(OA,Ie,ze,Ue);else{if(Re!==2)throw new Error(`Cannot use log_mel option '${Ge}' with power ${Re}`);(function($,me=1,Se=1e-10,qe=null){ne($,10,me,Se,qe)})(OA,Ie,ze,Ue)}break;default:throw new Error(`log_mel must be one of null, 'log', 'log10' or 'dB'. 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ne=0,we=0;ne<this.data.length;++ne)se[we++]=this.data[ne],se[we++]=this.data[ne],se[we++]=this.data[ne],se[we++]=255;break;case 3:for(let ne=0,we=0;ne<this.data.length;ne+=3)se[we++]=this.data[ne],se[we++]=this.data[ne+1],se[we++]=this.data[ne+2],se[we++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(se,this.width,this.height,4)}putAlpha(se){if(se.width!==this.width||se.height!==this.height)throw new Error(`Expected mask size to be ${this.width}x${this.height}, but got ${se.width}x${se.height}`);if(se.channels!==1)throw new Error(`Expected mask to have 1 channel, but got ${se.channels}`);const ne=this.data,we=se.data,g=this.width*this.height;if(this.channels===3){const b=new Uint8ClampedArray(4*g);for(let e=0,l=0,M=0;e<g;++e)b[M++]=ne[l++],b[M++]=ne[l++],b[M++]=ne[l++],b[M++]=we[e];return this._update(b,this.width,this.height,4)}if(this.channels===4){for(let b=0;b<g;++b)ne[4*b+3]=we[b];return 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Using bilinear instead.`),g="bilinear");case"nearest":case"bilinear":case"bicubic":l=l.affine([se/this.width,0,0,ne/this.height],{interpolator:g});break;case"lanczos":l=l.resize({width:se,height:ne,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${g} is not supported.`)}return await S(l)}}async pad([se,ne,we,g]){if(se=Math.max(se,0),ne=Math.max(ne,0),we=Math.max(we,0),g=Math.max(g,0),se===0&&ne===0&&we===0&&g===0)return this;if(X){const b=this.channels,e=this.toCanvas(),l=this.width+se+ne,M=this.height+we+g,ue=q(l,M).getContext("2d");return ue.drawImage(e,0,0,this.width,this.height,se,we,this.width,this.height),new be(ue.getImageData(0,0,l,M).data,l,M,4).convert(b)}{const b=this.toSharp().extend({left:se,right:ne,top:we,bottom:g});return await S(b)}}async crop([se,ne,we,g]){if(se=Math.max(se,0),ne=Math.max(ne,0),we=Math.min(we,this.width-1),g=Math.min(g,this.height-1),se===0&&ne===0&&we===this.width-1&&g===this.height-1)return this;const b=we-se+1,e=g-ne+1;if(X){const l=this.channels,M=this.toCanvas(),ue=q(b,e).getContext("2d");return ue.drawImage(M,se,ne,b,e,0,0,b,e),new be(ue.getImageData(0,0,b,e).data,b,e,4).convert(l)}{const l=this.toSharp().extract({left:se,top:ne,width:b,height:e});return await S(l)}}async center_crop(se,ne){if(this.width===se&&this.height===ne)return this;const we=(this.width-se)/2,g=(this.height-ne)/2;if(X){const b=this.channels,e=this.toCanvas(),l=q(se,ne).getContext("2d");let M=0,ue=0,D=0,z=0;return we>=0?M=we:D=-we,g>=0?ue=g:z=-g,l.drawImage(e,M,ue,se,ne,D,z,se,ne),new be(l.getImageData(0,0,se,ne).data,se,ne,4).convert(b)}{let b=this.toSharp();if(we>=0&&g>=0)b=b.extract({left:Math.floor(we),top:Math.floor(g),width:se,height:ne});else if(we<=0&&g<=0){const e=Math.floor(-g),l=Math.floor(-we);b=b.extend({top:e,left:l,right:se-this.width-l,bottom:ne-this.height-e})}else{let e=[0,0],l=0;g<0?(e[0]=Math.floor(-g),e[1]=ne-this.height-e[0]):l=Math.floor(g);let M=[0,0],ue=0;we<0?(M[0]=Math.floor(-we),M[1]=se-this.width-M[0]):ue=Math.floor(we),b=b.extend({top:e[0],bottom:e[1],left:M[0],right:M[1]}).extract({left:ue,top:l,width:se,height:ne})}return await S(b)}}async toBlob(se="image/png",ne=1){if(!X)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:se,quality:ne})}toTensor(se="CHW"){let ne=new O.Tensor("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(se!=="HWC"){if(se!=="CHW")throw new Error(`Unsupported channel format: ${se}`);ne=ne.permute(2,0,1)}return ne}toCanvas(){if(!X)throw new Error("toCanvas() is only supported in browser environments.");const se=this.clone().rgba(),ne=q(se.width,se.height),we=new R(se.data,se.width,se.height);return ne.getContext("2d").putImageData(we,0,0),ne}split(){const{data:se,width:ne,height:we,channels:g}=this,b=se.constructor,e=se.length/g,l=Array.from({length:g},()=>new b(e));for(let M=0;M<e;++M){const ue=g*M;for(let D=0;D<g;++D)l[D][M]=se[ue+D]}return l.map(M=>new be(M,ne,we,1))}_update(se,ne,we,g=null){return this.data=se,this.width=ne,this.height=we,g!==null&&(this.channels=g),this}clone(){return new be(this.data.slice(),this.width,this.height,this.channels)}convert(se){if(this.channels===se)return this;switch(se){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}async save(se){if(!X){if(j.apis.IS_FS_AVAILABLE)return await this.toSharp().toFile(se);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}{if(j.apis.IS_WEBWORKER_ENV)throw new Error("Unable to save an image from a Web Worker.");const ne=se.split(".").pop().toLowerCase(),we=re.get(ne)??"image/png",g=await this.toBlob(we);(0,k.saveBlob)(se,g)}}toSharp(){if(X)throw new Error("toSharp() is only supported in server-side environments.");return V(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}const xe=be.read.bind(be)},"./src/utils/maths.js":(x,C,d)=>{function k(g,[b,e,l],[M,ue],D="bilinear",z=!1){const Re=ue/l,Ne=M/e,Ye=new g.constructor(M*ue*b),Je=e*l,AA=M*ue;for(let xA=0;xA<M;++xA)for(let GA=0;GA<ue;++GA){const Ge=xA*ue+GA,Ie=(GA+.5)/Re-.5,ze=(xA+.5)/Ne-.5;let Ue=Math.floor(Ie),Pe=Math.floor(ze);const kA=Math.min(Ue+1,l-1),aA=Math.min(Pe+1,e-1);Ue=Math.max(Ue,0),Pe=Math.max(Pe,0);const iA=Ie-Ue,yA=ze-Pe,nA=(1-iA)*(1-yA),Fe=iA*(1-yA),Ze=(1-iA)*yA,mA=iA*yA,CA=Pe*l,Te=aA*l,pA=CA+Ue,EA=CA+kA,SA=Te+Ue,YA=Te+kA;for(let J=0;J<b;++J){const OA=J*Je;Ye[J*AA+Ge]=nA*g[OA+pA]+Fe*g[OA+EA]+Ze*g[OA+SA]+mA*g[OA+YA]}}return Ye}function P(g,b,e){const l=new Array(e.length),M=new Array(e.length);for(let z=e.length-1,Re=1;z>=0;--z)M[z]=Re,l[z]=b[e[z]],Re*=l[z];const ue=e.map((z,Re)=>M[e.indexOf(Re)]),D=new g.constructor(g.length);for(let z=0;z<g.length;++z){let Re=0;for(let Ne=b.length-1,Ye=z;Ne>=0;--Ne)Re+=Ye%b[Ne]*ue[Ne],Ye=Math.floor(Ye/b[Ne]);D[Re]=g[z]}return[D,l]}function j(g){const b=X(g)[0],e=g.map(ue=>Math.exp(ue-b)),l=e.reduce((ue,D)=>ue+D,0);return e.map(ue=>ue/l)}function O(g){const b=X(g)[0];let e=0;for(let ue=0;ue<g.length;++ue)e+=Math.exp(g[ue]-b);const l=Math.log(e);return g.map(ue=>ue-b-l)}function V(g,b){let e=0;for(let l=0;l<g.length;++l)e+=g[l]*b[l];return e}function q(g,b){return V(g,b)/(R(g)*R(b))}function R(g){return Math.sqrt(g.reduce((b,e)=>b+e*e,0))}function S(g){if(g.length===0)throw Error("Array must not be empty");let b=g[0],e=0;for(let l=1;l<g.length;++l)g[l]<b&&(b=g[l],e=l);return[b,e]}function X(g){if(g.length===0)throw Error("Array must not be empty");let b=g[0],e=0;for(let l=1;l<g.length;++l)g[l]>b&&(b=g[l],e=l);return[b,e]}function H(g){return g>0&&!(g&g-1)}d.r(C),d.d(C,{FFT:()=>xe,bankers_round:()=>ne,cos_sim:()=>q,dot:()=>V,dynamic_time_warping:()=>we,interpolate_data:()=>k,log_softmax:()=>O,magnitude:()=>R,max:()=>X,medianFilter:()=>ye,min:()=>S,permute_data:()=>P,round:()=>se,softmax:()=>j});class re{constructor(b){if(this.size=0|b,this.size<=1||!H(this.size))throw new Error("FFT size must be a power of two larger than 1");this._csize=b<<1,this.table=new Float64Array(2*this.size);for(let l=0;l<this.table.length;l+=2){const M=Math.PI*l/this.size;this.table[l]=Math.cos(M),this.table[l+1]=-Math.sin(M)}let e=0;for(let l=1;this.size>l;l<<=1)++e;this._width=e%2==0?e-1:e,this._bitrev=new Int32Array(1<<this._width);for(let l=0;l<this._bitrev.length;++l){this._bitrev[l]=0;for(let M=0;M<this._width;M+=2){const ue=this._width-M-2;this._bitrev[l]|=(l>>>M&3)<<ue}}}createComplexArray(){return new Float64Array(this._csize)}fromComplexArray(b,e){const l=e||new Array(b.length>>>1);for(let M=0;M<b.length;M+=2)l[M>>>1]=b[M];return l}toComplexArray(b,e){const l=e||this.createComplexArray();for(let M=0;M<l.length;M+=2)l[M]=b[M>>>1],l[M+1]=0;return l}transform(b,e){if(b===e)throw new Error("Input and output buffers must be different");this._transform4(b,e,1)}realTransform(b,e){if(b===e)throw new Error("Input and output buffers must be different");this._realTransform4(b,e,1)}inverseTransform(b,e){if(b===e)throw new Error("Input and output buffers must be different");this._transform4(b,e,-1);for(let l=0;l<b.length;++l)b[l]/=this.size}_transform4(b,e,l){const M=this._csize;let ue,D,z=1<<this._width,Re=M/z<<1;const Ne=this._bitrev;if(Re===4)for(ue=0,D=0;ue<M;ue+=Re,++D){const Je=Ne[D];this._singleTransform2(e,b,ue,Je,z)}else for(ue=0,D=0;ue<M;ue+=Re,++D){const Je=Ne[D];this._singleTransform4(e,b,ue,Je,z,l)}const Ye=this.table;for(z>>=2;z>=2;z>>=2){Re=M/z<<1;const Je=Re>>>2;for(ue=0;ue<M;ue+=Re){const AA=ue+Je-1;for(let xA=ue,GA=0;xA<AA;xA+=2,GA+=z){const Ge=xA,Ie=Ge+Je,ze=Ie+Je,Ue=ze+Je,Pe=b[Ge],kA=b[Ge+1],aA=b[Ie],iA=b[Ie+1],yA=b[ze],nA=b[ze+1],Fe=b[Ue],Ze=b[Ue+1],mA=Ye[GA],CA=l*Ye[GA+1],Te=aA*mA-iA*CA,pA=aA*CA+iA*mA,EA=Ye[2*GA],SA=l*Ye[2*GA+1],YA=yA*EA-nA*SA,J=yA*SA+nA*EA,OA=Ye[3*GA],ge=l*Ye[3*GA+1],$=Fe*OA-Ze*ge,me=Fe*ge+Ze*OA,Se=Pe+YA,qe=kA+J,dA=Pe-YA,oA=kA-J,BA=Te+$,QA=pA+me,TA=l*(Te-$),Mt=l*(pA-me);b[Ge]=Se+BA,b[Ge+1]=qe+QA,b[Ie]=dA+Mt,b[Ie+1]=oA-TA,b[ze]=Se-BA,b[ze+1]=qe-QA,b[Ue]=dA-Mt,b[Ue+1]=oA+TA}}}}_singleTransform2(b,e,l,M,ue){const D=b[M],z=b[M+1],Re=b[M+ue],Ne=b[M+ue+1];e[l]=D+Re,e[l+1]=z+Ne,e[l+2]=D-Re,e[l+3]=z-Ne}_singleTransform4(b,e,l,M,ue,D){const z=2*ue,Re=3*ue,Ne=b[M],Ye=b[M+1],Je=b[M+ue],AA=b[M+ue+1],xA=b[M+z],GA=b[M+z+1],Ge=b[M+Re],Ie=b[M+Re+1],ze=Ne+xA,Ue=Ye+GA,Pe=Ne-xA,kA=Ye-GA,aA=Je+Ge,iA=AA+Ie,yA=D*(Je-Ge),nA=D*(AA-Ie);e[l]=ze+aA,e[l+1]=Ue+iA,e[l+2]=Pe+nA,e[l+3]=kA-yA,e[l+4]=ze-aA,e[l+5]=Ue-iA,e[l+6]=Pe-nA,e[l+7]=kA+yA}_realTransform4(b,e,l){const M=this._csize;let ue,D,z=1<<this._width,Re=M/z<<1;const Ne=this._bitrev;if(Re===4)for(ue=0,D=0;ue<M;ue+=Re,++D){const AA=Ne[D];this._singleRealTransform2(e,b,ue,AA>>>1,z>>>1)}else for(ue=0,D=0;ue<M;ue+=Re,++D){const AA=Ne[D];this._singleRealTransform4(e,b,ue,AA>>>1,z>>>1,l)}const Ye=this.table;for(z>>=2;z>=2;z>>=2){Re=M/z<<1;const AA=Re>>>1,xA=AA>>>1,GA=xA>>>1;for(ue=0;ue<M;ue+=Re)for(let Ge=0,Ie=0;Ge<=GA;Ge+=2,Ie+=z){const ze=ue+Ge,Ue=ze+xA,Pe=Ue+xA,kA=Pe+xA,aA=b[ze],iA=b[ze+1],yA=b[Ue],nA=b[Ue+1],Fe=b[Pe],Ze=b[Pe+1],mA=b[kA],CA=b[kA+1],Te=aA,pA=iA,EA=Ye[Ie],SA=l*Ye[Ie+1],YA=yA*EA-nA*SA,J=yA*SA+nA*EA,OA=Ye[2*Ie],ge=l*Ye[2*Ie+1],$=Fe*OA-Ze*ge,me=Fe*ge+Ze*OA,Se=Ye[3*Ie],qe=l*Ye[3*Ie+1],dA=mA*Se-CA*qe,oA=mA*qe+CA*Se,BA=Te+$,QA=pA+me,TA=Te-$,Mt=pA-me,Tt=YA+dA,ut=J+oA,Ct=l*(YA-dA),$t=l*(J-oA);if(b[ze]=BA+Tt,b[ze+1]=QA+ut,b[Ue]=TA+$t,b[Ue+1]=Mt-Ct,Ge===0){b[Pe]=BA-Tt,b[Pe+1]=QA-ut;continue}if(Ge===GA)continue;const Qr=ue+xA-Ge,tr=ue+AA-Ge;b[Qr]=TA-l*$t,b[Qr+1]=-Mt-l*Ct,b[tr]=BA-l*Tt,b[tr+1]=l*ut-QA}}const Je=M>>>1;for(let AA=2;AA<Je;AA+=2)b[M-AA]=b[AA],b[M-AA+1]=-b[AA+1]}_singleRealTransform2(b,e,l,M,ue){const D=b[M],z=b[M+ue];e[l]=D+z,e[l+1]=0,e[l+2]=D-z,e[l+3]=0}_singleRealTransform4(b,e,l,M,ue,D){const z=2*ue,Re=3*ue,Ne=b[M],Ye=b[M+ue],Je=b[M+z],AA=b[M+Re],xA=Ne+Je,GA=Ne-Je,Ge=Ye+AA,Ie=D*(Ye-AA);e[l]=xA+Ge,e[l+1]=0,e[l+2]=GA,e[l+3]=-Ie,e[l+4]=xA-Ge,e[l+5]=0,e[l+6]=GA,e[l+7]=Ie}}class be{constructor(b){const e=2*(b-1),l=2*(2*b-1),M=2**Math.ceil(Math.log2(l));this.bufferSize=M,this._a=e;const ue=new Float64Array(l),D=new Float64Array(M);this._chirpBuffer=new Float64Array(M),this._buffer1=new Float64Array(M),this._buffer2=new Float64Array(M),this._outBuffer1=new Float64Array(M),this._outBuffer2=new Float64Array(M);const z=-2*Math.PI/b,Re=Math.cos(z),Ne=Math.sin(z);for(let Ye=0;Ye<l>>1;++Ye){const Je=(Ye+1-b)**2/2,AA=Math.sqrt(Re**2+Ne**2)**Je,xA=Je*Math.atan2(Ne,Re),GA=2*Ye;ue[GA]=AA*Math.cos(xA),ue[GA+1]=AA*Math.sin(xA),D[GA]=ue[GA],D[GA+1]=-ue[GA+1]}this._slicedChirpBuffer=ue.subarray(e,l),this._f=new re(M>>1),this._f.transform(this._chirpBuffer,D)}_transform(b,e,l){const M=this._buffer1,ue=this._buffer2,D=this._outBuffer1,z=this._outBuffer2,Re=this._chirpBuffer,Ne=this._slicedChirpBuffer,Ye=this._a;if(l)for(let Je=0;Je<Ne.length;Je+=2){const AA=Je+1,xA=e[Je>>1];M[Je]=xA*Ne[Je],M[AA]=xA*Ne[AA]}else for(let Je=0;Je<Ne.length;Je+=2){const AA=Je+1;M[Je]=e[Je]*Ne[Je]-e[AA]*Ne[AA],M[AA]=e[Je]*Ne[AA]+e[AA]*Ne[Je]}this._f.transform(D,M);for(let Je=0;Je<Re.length;Je+=2){const AA=Je+1;ue[Je]=D[Je]*Re[Je]-D[AA]*Re[AA],ue[AA]=D[Je]*Re[AA]+D[AA]*Re[Je]}this._f.inverseTransform(z,ue);for(let Je=0;Je<z.length;Je+=2){const AA=z[Je+Ye],xA=z[Je+Ye+1],GA=Ne[Je],Ge=Ne[Je+1];b[Je]=AA*GA-xA*Ge,b[Je+1]=AA*Ge+xA*GA}}transform(b,e){this._transform(b,e,!1)}realTransform(b,e){this._transform(b,e,!0)}}class xe{constructor(b){this.fft_length=b,this.isPowerOfTwo=H(b),this.isPowerOfTwo?(this.fft=new re(b),this.outputBufferSize=2*b):(this.fft=new be(b),this.outputBufferSize=this.fft.bufferSize)}realTransform(b,e){this.fft.realTransform(b,e)}transform(b,e){this.fft.transform(b,e)}}function ye(g,b){if(b%2==0||b<=0)throw new Error("Window size must be a positive odd number");const e=new g.constructor(g.length),l=new g.constructor(b),M=Math.floor(b/2);for(let ue=0;ue<g.length;++ue){let D=0;for(let z=-M;z<=M;++z){let Re=ue+z;Re<0?Re=Math.abs(Re):Re>=g.length&&(Re=2*(g.length-1)-Re),l[D++]=g[Re]}l.sort(),e[ue]=l[M]}return e}function se(g,b){const e=Math.pow(10,b);return Math.round(g*e)/e}function ne(g){const b=Math.round(g);return Math.abs(g)%1==.5?b%2==0?b:b-1:b}function we(g){const b=g.length,e=g[0].length,l=[b+1,e+1],M=Array.from({length:l[0]},()=>Array(l[1]).fill(1/0));M[0][0]=0;const ue=Array.from({length:l[0]},()=>Array(l[1]).fill(-1));for(let Ye=1;Ye<l[1];++Ye)for(let Je=1;Je<l[0];++Je){const AA=M[Je-1][Ye-1],xA=M[Je-1][Ye],GA=M[Je][Ye-1];let Ge,Ie;AA<xA&&AA<GA?(Ge=AA,Ie=0):xA<AA&&xA<GA?(Ge=xA,Ie=1):(Ge=GA,Ie=2),M[Je][Ye]=g[Je-1][Ye-1]+Ge,ue[Je][Ye]=Ie}for(let Ye=0;Ye<l[1];++Ye)ue[0][Ye]=2;for(let Ye=0;Ye<l[0];++Ye)ue[Ye][0]=1;let D=b,z=e,Re=[],Ne=[];for(;D>0||z>0;)switch(Re.push(D-1),Ne.push(z-1),ue[D][z]){case 0:--D,--z;break;case 1:--D;break;case 2:--z;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${D}, ${z}]. 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A}e[33284]=e[33284]+1,A=e[33283],r=0|ct[e[A+8>>2]](A)}e[o+2152>>2]=r}Di=MA+2|0,Ke=32;e:{for(;;){We=Ke;A:{r:{s:{if(r=e[33691],(s=e[33285])?A=0:(s=e[33285],A=e[33283],A=e[A>>2]==e[A+4>>2]),r|!A|s||!(e[32524]<0)){if(!Vi(e[o+2156>>2])){if((s=(0|(A=e[49828]))>0)&(0|(r=A))<(0|(A=e[33284])))break s;if(!((0|(r=e[49845]))<=0|(0|A)<(0|r))){e[49845]=0,g[134760]=1,e[33285]=e[o+2152>>2],a=16384;break e}}Ke=e[o+2156>>2],e[o+2156>>2]=e[o+2152>>2];i:{c:{o:{u:{l:{if((0|(r=e[32524]))>=0){if(l[r+134736|0])break l;e[32524]=-1}if(r=e[33285])break o;if(A=e[33283],e[A>>2]!=e[A+4>>2])break u;r=32;break i}e[33691]|r||(e[o+2156>>2]=g[134736],r=1),e[32524]=r+1,r=g[r+134736|0];break i}if(!(r=e[33285]))break c}e[33285]=0;break i}e[33284]=e[33284]+1,A=e[33283],r=0|ct[e[A+8>>2]](A)}e[o+2152>>2]=r,e[33691]=0;i:if(!(m|!e[47203])){c:{if((0|(A=e[o+2156>>2]))!=60){if((0|r)!=35&r-97>>>0>25|(0|A)!=38)break i;for(s=e[33285],i=0;;){o:{if(e[o+2156>>2]=r,!s){if(A=e[33283],e[A>>2]==e[A+4>>2])break o;r=e[o+2156>>2]}if(!(!((r=!!(0|Vi(r)))|(0|(A=e[o+2156>>2]))==35)|i>>>0>19)){g[(o+112|0)+i|0]=A,i=i+1|0,(r=e[33285])?(e[33285]=0,s=0):(e[33284]=e[33284]+1,A=e[33283],r=0|ct[e[A+8>>2]](A),s=e[33285]);continue}}break}g[(o+112|0)+i|0]=0;o:{u:{if(!(r=e[33285])){if(r=0,A=e[33283],e[A>>2]==e[A+4>>2])break o;if(!(r=e[33285]))break u}e[33285]=0;break o}e[33284]=e[33284]+1,A=e[33283],r=0|ct[e[A+8>>2]](A)}e[o+2152>>2]=r,e[o+100>>2]=e[o+2156>>2],e[o+104>>2]=r,e[o+96>>2]=o+112,DA(134736,84252,o+96|0);o:{if(e[o+2156>>2]==59){i=o+2156|0,L=o+2152|0,J=s=J-32|0;u:if(l[0|(r=o+112|0)]!=35)A=-1,(0|(r=Cs(130752,r)))!=-1&&(e[i>>2]=r,e[L>>2]||(e[L>>2]=32),A=r);else{if(l[0|(A=r+1|0)]==120){e[s>>2]=i,A=ut(r+2|0,90005,s);break u}e[s+16>>2]=i,A=ut(A,90070,s+16|0)}if(J=s+32|0,(0|A)>0)break o}e[32524]=0,e[o+2156>>2]=38,e[o+2152>>2]=32;break i}if((0|(A=e[o+2156>>2]))>32)break i;if(!(r=e[33692]-20|0)|(0|r)==16)break c;break i}if((0|r)!=47&&!is(r)&&(0|(A=e[o+2152>>2]))!=63&&(0|A)!=33)break i;if((0|(A=e[o+2148>>2]))>780){e[33691]=e[o+2156>>2],g[0|(A=A+189424|0)]=32,g[A+1|0]=0,e[33285]=e[o+2152>>2],a=16384;break e}for(L=e[33285],i=0,r=e[o+2152>>2];e[o+2156>>2]=r,s=0,L||(A=e[33283],s=e[A>>2]==e[A+4>>2],r=e[o+2156>>2]),!((0|r)==62|s|i>>>0>499);)e[(o+144|0)+(i<<2)>>2]=r,i=i+1|0,(r=e[33285])?(L=0,e[33285]=0):(e[33284]=e[33284]+1,A=e[33283],r=0|ct[e[A+8>>2]](A),L=e[33285]);e[(s=o+144|0)+(i<<2)>>2]=0,e[o+2152>>2]=32,rA=o+2148|0,We=e[32525],r=0,J=Qe=J-560|0;o:if(En(s,84333,3)&&En(s,84477,4)){for(A=(s+(B0(s)<<2)|0)-4|0,(0|(zA=e[A>>2]))==47&&(e[A>>2]=32);;){if(A=e[s+(r<<2)>>2]){if(Or(A))A=r;else if(g[(Qe+512|0)+r|0]=Ht(A<<24>>24),A=39,(0|(r=r+1|0))!=39)continue}else A=r;break}if(g[(Qe+512|0)+A|0]=0,l[Qe+512|0]!=47){if((0|(L=Cs(130480,Qe+512|0)))!=16&&(r=e[rA>>2],e[rA>>2]=r+1,g[r+189424|0]=32),(0|zA)==47&&(r=0,!(502241>>>L&1)))break o}else(0|(r=Cs(130480,Qe+512|1)))!=16&&(i=e[rA>>2],e[rA>>2]=i+1,g[i+189424|0]=32),L=r+32|0;i=s+(A<<2)|0,A=e[33708],m=z(A,76)+133076|0,r=262174;u:{l:{n:{p:switch(L-1|0){case 33:C:if(!((0|A)<=1)){for(;;){if(e[z(r=A-1|0,76)+133152>>2]==2)break C;if(e[33708]=r,s=A>>>0>2,A=r,!s)break}A=1}r=Ea(i,34,A);break o;case 32:C:if(!((0|A)<=1)){for(;;){if(e[z(r=A-1|0,76)+133152>>2]==1)break C;if(e[33708]=r,s=A>>>0>2,A=r,!s)break}A=1}r=Ea(i,33,A)+524328|0;break o;case 9:(0|(r=e[33709]))<=18&&(e[33709]=r+1),e[(A=134912+(m=r<<6)|0)>>2]=10,e[A+4>>2]=-1,e[A+8>>2]=-1,e[A+52>>2]=-1,e[A+56>>2]=-1,e[A+44>>2]=-1,e[A+48>>2]=-1,e[A+36>>2]=-1,e[A+40>>2]=-1,e[A+28>>2]=-1,e[A+32>>2]=-1,e[A+20>>2]=-1,e[A+24>>2]=-1,e[A+12>>2]=-1,e[A+16>>2]=-1,e[A+60>>2]=-1,A=Ut(i,88301),s=Ut(i,88390),Bn(A,88479)?Bn(A,88528)||(e[28+(134912+(r<<6)|0)>>2]=ka(s,130224)):e[24+(m+134912|0)>>2]=ka(s,130192),qa(rA,e[33709]);break n;case 2:for((0|(A=e[33709]))<=18&&(e[33709]=A+1),e[(A=134912+(Gi=A<<6)|0)>>2]=3,e[A+4>>2]=-1,e[A+8>>2]=-1,e[A+52>>2]=-1,e[A+56>>2]=-1,e[A+44>>2]=-1,e[A+48>>2]=-1,e[A+36>>2]=-1,e[A+40>>2]=-1,e[A+28>>2]=-1,e[A+32>>2]=-1,e[A+20>>2]=-1,e[A+24>>2]=-1,e[A+12>>2]=-1,e[A+16>>2]=-1,e[A+60>>2]=-1,er=1;;){if(s=Ut(i,e[130448+(pr=er<<2)>>2])){zA=0,A=e[pr+131072>>2];C:if(L=e[A>>2])for(;;){for(r=0;m=g[r+L|0],(We=e[(r<<2)+s>>2])&&(r=r+1|0,(0|m)==(0|We)););switch(We-34|0){case 0:case 5:if(!m)break C}if(!(L=e[A+((zA=zA+1|0)<<3)>>2]))break}Un=pr+(Gi+134912|0)|0;C:if((0|(A=e[4+(A+(zA<<3)|0)>>2]))>=0)A=(0|z(A,e[4+(pr+134912|0)>>2]))/100|0;else{for(;s=(A=s)+4|0,Or(e[A>>2]););for(di=e[A>>2]==43,s=((fi=e[(A=A+(di<<2)|0)>>2]==45)<<2)+A|0,zA=Qe+96|0,J=L=(J=m=J-16|0)-224|0,jA(L+16|0,0,144),A=L+160|4,e[L+24>>2]=A,e[L+60>>2]=A,e[L+92>>2]=-1,e[L+64>>2]=60,e[L+20>>2]=A,e[L+48>>2]=19,r=s;r=(A=r)+4|0,We=(We=e[A>>2])?Lr(124960,We):0;);e[L+100>>2]=A,Ja(r=L+16|0,0,0),BA(L,r,1,1),r=e[L+8>>2],Q0=e[L+12>>2],We=e[L>>2],T0=e[L+4>>2],zA&&(Ji=zA,zA=e[L+136>>2]+(e[L+20>>2]-e[L+60>>2]|0)|0,e[Ji>>2]=zA?A+(zA<<2)|0:s),e[(A=m)+8>>2]=r,e[A+12>>2]=Q0,e[A>>2]=We,e[A+4>>2]=T0,J=L+224|0,ve=ys(e[A>>2],e[A+4>>2],e[A+8>>2],e[A+12>>2]),J=A+16|0;m:{r=100;b:if((0|(A=s))!=(0|(s=e[Qe+96>>2]))){r=fi?-1:di;h:{if((0|(A=e[s>>2]))!=115){if((0|A)!=37)break h;if(Ne(ve=r?+(0|r)*ve+100:ve)<2147483648){r=~~ve;break b}r=-2147483648;break b}if(e[s+4>>2]==116){be(+(Ft=ve*+(0|r)/12)),A=0|X(1),X(0);E:{if((r=(A=A>>>20&2047)-969|0)>>>0>=63){if(ve=Ft+1,(0|r)<0)break E;if(be(+Ft),r=0|X(1),s=0|X(0),!(A>>>0<1033)){if(ve=0,!s&(0|r)==-1048576||(ve=Ft+1,A>>>0>=2047))break E;if((0|r)>0|(0|r)>=0){D[8+(A=J-16|0)>>3]=3105036184601418e216,ve=3105036184601418e216*D[A+8>>3];break E}if(!(r>>>0<3230714880)){D[8+(A=J-16|0)>>3]=12882297539194267e-247,ve=12882297539194267e-247*D[A+8>>3];break E}}m=A,A=!(s<<1)&(0|(A=r<<1|s>>>31))==-2129002496|A>>>0<2165964800?m:0}Ba=(Ft=(ve=Ft-((ia=(ve=D[14416])+Ft)-ve))*ve)*Ft*(ve*D[14421]+D[14420]),Ft*=ve*D[14419]+D[14418],ve*=D[14417],be(+ia),X(1),m=0|X(0),ve=Ba+(Ft+(ve+D[115376+(s=m<<4&2032)>>3])),We=e[(s=s+115384|0)>>2],L=e[s+4>>2],s=(r=We)+(We=0)|0,r=(m<<13)+L|0,r=s>>>0<We>>>0?r+1|0:r,A?(H(0,0|s),H(1,0|r),ve=(Ft=+re())*ve+Ft):-2147483648&m?(H(0,0|s),H(1,r+1071644672|0),(ve=(ia=(Ft=+re())*ve)+Ft)<1&&(e[8+(A=J-16|0)>>2]=0,e[A+12>>2]=1048576,D[A+8>>3]=22250738585072014e-324*D[A+8>>3],ve=(ve=(Ba=ve+1)+(ia+(Ft-ve)+(ve+(1-Ba)))+-1)==0?0:ve),ve*=22250738585072014e-324):(H(0,0|s),H(1,r+-1048576|0),ve=(Ft=+re())*ve+Ft,ve+=ve)}if(Ne(ve*=100)<2147483648){r=~~ve;break b}r=-2147483648;break b}}if((0|er)!=1)break m;if(r)r=100+(A=Ne(ve=ve*+(0|r)*100)<2147483648?~~ve:-2147483648)|0;else{if(Ne(ve*=100)<2147483648){r=~~ve;break b}r=-2147483648}}A=(0|z(r,e[pr+134848>>2]))/100|0;break C}A=Ne(ve)<2147483648?~~ve:-2147483648,r&&(A=e[pr+134848>>2]+z(A,r)|0)}e[Un+4>>2]=A}if((0|(er=er+1|0))==5)break}qa(rA,e[33709]);break n;case 11:(0|(r=e[33709]))<=18&&(e[33709]=r+1),e[(A=134912+(r<<6)|0)>>2]=12,e[A+4>>2]=-1,e[A+8>>2]=-1,e[A+52>>2]=-1,e[A+56>>2]=-1,e[A+44>>2]=-1,e[A+48>>2]=-1,e[A+36>>2]=-1,e[A+40>>2]=-1,e[A+28>>2]=-1,e[A+32>>2]=-1,e[A+20>>2]=-1,e[A+24>>2]=-1,e[A+12>>2]=-1,e[A+16>>2]=-1,e[A+60>>2]=-1,A=(A=Ut(i,88658))?ka(A,130400):3,s=134912+(r<<6)|0,e[e[47192]+148>>2]!=1?(e[52+(134912+(r<<6)|0)>>2]=A,A=l[A+102776|0]):(e[20+(134912+(r<<6)|0)>>2]=l[A+102764|0],A=l[A+102770|0]),e[s+12>>2]=A,qa(rA,e[33709]);break n;case 34:case 41:case 43:if(!((0|(A=e[33709]))<=0)){if(i=L-32|0,L=0,s=0,r=0,A>>>0>=4)for(m=-4&A,zA=0;er=2|r,pr=1|r,s=(0|i)==e[134912+((We=3|r)<<6)>>2]?We:(0|i)==e[134912+(er<<6)>>2]?er:(0|i)==e[134912+(pr<<6)>>2]?pr:(0|i)==e[134912+(r<<6)>>2]?r:s,r=r+4|0,(0|m)!=(0|(zA=zA+4|0)););if(m=3&A)for(;s=(0|i)==e[134912+(r<<6)>>2]?r:s,r=r+1|0,(0|m)!=(0|(L=L+1|0)););(0|s)<=0||(e[33709]=s,A=s)}qa(rA,A);break n;case 7:if(A=Ut(i,88741),r=Ut(i,88860),(0|ka(A,130176))!=1)break n;A=e[rA>>2],e[rA>>2]=A+1,g[A+189424|0]=91,A=e[rA>>2],e[rA>>2]=A+1,g[A+189424|0]=91,A=Ta((A=e[rA>>2])+189424|0,r,800-A|0)+e[rA>>2]|0,e[rA>>2]=A+1,g[A+189424|0]=93,A=e[rA>>2],e[rA>>2]=A+1,g[A+189424|0]=93;break n;case 35:e[33692]==36&&(g[e[rA>>2]+189424|0]=0,(s=Cs(131104,r=(A=e[33707])+189424|0))&&(e[rA>>2]=zr(s,r)+A)),A=e[rA>>2],e[rA>>2]=A+1,g[A+189424|0]=1,A=e[rA>>2],e[rA>>2]=A+1,g[A+189424|0]=89,e[33692]=0;break n;case 8:if(!(A=Ut(i,89299)))break n;g[134824]=1,r=e[rA>>2],e[rA>>2]=Ta(r+189424|0,A,800-r|0)+e[rA>>2];break n;case 13:g[134824]=1;break n;case 40:case 45:g[134824]=0;break n;case 4:if(!(A=Ut(i,89360)))break n;if(Ta(Qe+352|0,A,160),l[Qe+352|0]&&!vr(199328,Qe+352|0)){g[134760]=1,g[199328]=0,r=16384;break o}if((0|(A=Ri(Qe+352|0)))<0)break n;e[Qe+20>>2]=A,e[Qe+16>>2]=1,DA(A=Qe+352|0,89460,Qe+16|0),qA(e[rA>>2]+189424|0,A),e[rA>>2]=e[rA>>2]+HA(A);break n;case 10:(0|(r=e[33709]))<=18&&(e[33709]=r+1),e[(A=134912+(r<<6)|0)>>2]=11,e[A+4>>2]=-1,e[A+8>>2]=-1,e[A+52>>2]=-1,e[A+56>>2]=-1,e[A+44>>2]=-1,e[A+48>>2]=-1,e[A+36>>2]=-1,e[A+40>>2]=-1,e[A+28>>2]=-1,e[A+32>>2]=-1,e[A+20>>2]=-1,e[A+24>>2]=-1,e[A+12>>2]=-1,e[A+16>>2]=-1,e[A+60>>2]=-1;C:if(A=Ut(i,89514)){if(Ta(Qe+352|0,A,160),e[34441]){if((0|(A=Ri(Qe+352|0)))<0||0|ct[e[34441]](1,A+e[33282]|0,We))break C;e[Qe+68>>2]=A,e[Qe+64>>2]=1,DA(Qe+352|0,89658,Qe- -64|0)}else{if(!We|l[Qe+352|0]==47?A=g0(Qe+352|0):(e[Qe+48>>2]=We,e[Qe+52>>2]=Qe+352,DA(A=Qe+96|0,89564,Qe+48|0),A=g0(A)),(0|A)<0)break C;e[Qe+36>>2]=A,e[Qe+32>>2]=1,DA(Qe+352|0,89623,Qe+32|0)}A=Qe+352|0,qA(e[rA>>2]+189424|0,A),e[rA>>2]=e[rA>>2]+HA(A),e[4+(134912+(r<<6)|0)>>2]=1}if(qa(rA,e[33709]),(0|zA)==47){s0(11,rA),r=16384;break o}g[134772]=1,r=16384;break o;case 42:s0(43,rA),g[134772]=0,r=16384;break o;case 12:C:{if(A=Ut(i,89714)){if(r=16384,(m=(0|(A=ka(A,130336)))<0?2:A)>>>0<=2&&(A=e[rA>>2],e[Qe+84>>2]=m,e[Qe+80>>2]=1,DA(A+189424|0,89770,Qe+80|0),e[rA>>2]=e[rA>>2]+3,r=0),A=e[102784+(m<<2)>>2],!(s=Ut(i,89907)))break C;break l}if(r=16384,s=Ut(i,89907))break l;A=21;break u}if(m>>>0<3)break n;break u;case 0:(A=Ut(i,89965))&&(Ta(r=Qe+352|0,A,160),Ri(r)),r=Ea(i,1,e[33708])?147456:0;break o;case 1:r=Ea(i,2,A)?147456:0;break o;case 5:r=0,e[m>>2]==6&&(r=Ea(i,38,A),A=e[33708]),r=524358+(Ea(i,6,A)|r)|0;break o;case 6:r=0,(0|(s=e[m>>2]))==6&&(r=Ea(i,38,A),s=e[m>>2]),(0|s)==7&&(r=Ea(i,39,e[33708])|r),r=524358+(r|Ea(i,7,e[33708]))|0;break o;case 37:if(r=524328,e[m>>2]!=6)break o;r=Ea(i,38,A)+524328|0;break o;case 38:if(r=524358,(-2&e[m>>2])!=6)break o;r=Ea(i,39,A)+524358|0;break o;case 14:case 46:break o;case 3:break p;default:break n}A=Ut(i,88893),r=Ut(i,88992),s=Ut(i,89153),i=ka(A,130272),r=ka(r,130320),A=C0(s,0),e[Qe>>2]=1,s=(0|A)<2?193:A- -64|0,r=(0|(A=(0|r)==1?19:i))==64?s:A,e[Qe+4>>2]=r,DA(A=Qe+352|0,89230,Qe),qA(e[rA>>2]+189424|0,A),A=e[rA>>2]+HA(A)|0,e[rA>>2]=A,e[33707]=A,e[33692]=r}r=0;break o}A=C0(s,1),s=e[33722],Gn(1,e[33713]),(0|(A=(0|(s=(0|z(A,s))/100<<8))/(0|z(e[36429],10))|0))<=199&&(A=(0|s)/(0|z(e[36428],10))|0),r=r||16384}s=A>>>5|0,i=A,r=((A=(0|A)>4095)?s>>>0>=4095?4095:s:i)+(A?8388608|r:r)|0}if(J=Qe+560|0,r){if(A=e[o+2148>>2]+189424|0,g[0|A]=32,g[A+1|0]=0,!(131072&r)){a=r;break e}qA(189360,134784),a=r;break e}e[o+2156>>2]=32;o:{u:{if(!(r=e[33285])){if(m=0,A=e[33283],e[A>>2]==e[A+4>>2])continue;if(!(r=e[33285]))break u}e[33285]=0;break o}e[33284]=e[33284]+1,A=e[33283],r=0|ct[e[A+8>>2]](A)}e[o+2152>>2]=r,m=0;continue}e[o+2156>>2]=A+57344}if(l[134824])continue;if(s=e[o+2156>>2],!((0|(A=e[o+2152>>2]))!=10|e[47268]!=-1)){(0|(a=Ls(s)))==16384?(A=e[o+2148>>2],b[MA+(A<<1)>>1]=e[33284]-e[47353],e[Wr>>2]=A,a=524328,A=zr(e[o+2156>>2],A+189424|0)+e[o+2148>>2]|0):A=e[o+2148>>2],g[0|(A=A+189424|0)]=32,g[A+1|0]=0;break e}i:if((0|s)==1){if((0|A)!=66){if((0|A)!=86)break i;for(A=e[o+2148>>2],e[o+2148>>2]=A+1,g[A+189424|0]=0;;){c:{o:{u:{if(!(r=e[33285])){if(A=e[33283],e[A>>2]==e[A+4>>2])break c;if(!(r=e[33285]))break u}e[33285]=0;break o}e[33284]=e[33284]+1,A=e[33283],r=0|ct[e[A+8>>2]](A)}if(e[o+2156>>2]=r,!(Or(r)||(0|(A=e[o+2148>>2]))>=799)){e[o+2148>>2]=A+1,g[A+189424|0]=e[o+2156>>2];continue}}break}g[e[o+2148>>2]+189424|0]=0,a=147456;break e}r=e[o+2148>>2],g[0|(A=r+189424|0)]=32,g[A+1|0]=32,g[A+2|0]=32,g[A+3|0]=0,e[o+2148>>2]=r+3;c:{o:{u:{l:{n:{p:{if(!(r=e[33285])){if(A=e[33283],e[A>>2]==e[A+4>>2])break l;if(!(r=e[33285]))break p}e[33285]=0;break n}e[33284]=e[33284]+1,A=e[33283],r=0|ct[e[A+8>>2]](A)}if(e[o+2152>>2]=r,s=0,(0|r)!=48)break u;break o}r=e[o+2152>>2]}if(e[47208]=0,e[47201]=1,(0|r)==49)break c;for(s=e[33285],i=0;;){u:{if(!s){if(A=e[33283],e[A>>2]==e[A+4>>2])break u;r=e[o+2152>>2]}if(!(Or(r)|i>>>0>58)){e[188832+(i<<2)>>2]=e[o+2152>>2],(r=e[33285])?(e[33285]=0,s=0):(e[33284]=e[33284]+1,A=e[33283],r=0|ct[e[A+8>>2]](A),s=e[33285]),i=i+1|0,e[o+2152>>2]=r,A=e[o+2148>>2],e[o+2148>>2]=A+1,g[A+189424|0]=32;continue}}break}s=2,e[188832+(i<<2)>>2]=0}e[47201]=s}c:{if(!(r=e[33285])){if(A=e[33283],e[A>>2]==e[A+4>>2])continue;if(!(r=e[33285]))break c}e[33285]=0,e[o+2152>>2]=r;continue}e[33284]=e[33284]+1,A=e[33283],e[o+2152>>2]=ct[e[A+8>>2]](A);continue}yr=yr+1|0,r=0,A=e[Z+340>>2];i:if(i=M[A>>1]){for(;;){if((65535&i)!=(0|s)){if(i=M[A+((r=r+2|0)<<1)>>1])continue;break i}break}switch(0|(A=M[A+(r<<1|2)>>1])){case 1:continue;case 0:break i}e[o+2156>>2]=A,s=A}Vi(s)?(ce=1,r=e[o+2156>>2]):at?(e[33285]=e[o+2152>>2],r=1328,e[o+2156>>2]=1328,e[o+2152>>2]=32,at=0):(0|(r=e[o+2156>>2]))!=3851?(at=0,(0|r)!=3405|e[o+2152>>2]!=8205||(r=3406,e[o+2156>>2]=3406)):(r=32,e[o+2156>>2]=32,at=0);i:if(hs(r)){if(e[Z+8216>>2]=e[Z+8216>>2]+1,e[33692]|e[47200]!=2||hs(Ke)||(e[o+2544>>2]=0,e[o+2548>>2]=0,e[o+2304>>2]=84731,!kr(Z,o+2304|0,o+2160|0,o+2544|0,0,0))||($t(Z,A=o+2160|0,o+2544|0,-1,0),JA(r=A,A=o+2336|0),e[o+80>>2]=A,DA(r=o+2240|0,85451,o+80|0),A=e[o+2148>>2],(0|(r=HA(r)+A|0))>=800))break i;qA(A+189424|0,o+2240|0),e[o+2148>>2]=r}else is(e[o+2156>>2])&&(e[Z+8220>>2]=e[Z+8220>>2]+1);if(r=e[o+2152>>2],A=e[o+2156>>2],e[47204])if((0|m)>0)m=m-1|0;else{if(!((0|A)!=91|(0|r)!=91)){s=0,m=-1;break A}m=(0|A)==93&&(0|r)==93?2:m}if((0|A)==10){for(s=e[33285],i=0;;){i:{if(!s){if(A=e[33283],e[A>>2]==e[A+4>>2])break i;r=e[o+2152>>2]}if(Or(r)){i=(e[o+2152>>2]==10)+i|0,(r=e[33285])?(e[33285]=0,s=0):(e[33284]=e[33284]+1,A=e[33283],r=0|ct[e[A+8>>2]](A),s=e[33285]),e[o+2152>>2]=r;continue}}break}if((0|i)>0){a&&jA(A=Er+189424|0,32,Gt(o+2336|0,A)),A=e[o+2148>>2]+189424|0,g[0|A]=32,g[A+1|0]=0,e[33285]=e[o+2152>>2],a=e[47203]?524358:z((0|i)>=3?3:i,30)+524328|0;break e}if(A=e[47268]<(0|yr),yr=0,!A){A=e[o+2148>>2]+189424|0,g[0|A]=32,g[A+1|0]=0,e[33285]=e[o+2152>>2],a=262174;break e}}if(s=0,e[33692]|m)break A;if(A=0,!a)break r;if(Or(e[o+2156>>2])){A=a;break r}if(rr(e[o+2156>>2])&&ms(e[o+2156>>2]))break r;e[33691]=e[o+2156>>2],g[0|(A=Er+189424|0)]=32,g[A+1|0]=0,e[33285]=e[o+2152>>2];break e}at&&(e[o+2148>>2]=zr(1328,e[o+2148>>2]+189424|0)+e[o+2148>>2]),a&&jA(A=Er+189424|0,32,Gt(o+2336|0,A)),A=e[o+2148>>2]+189424|0,g[0|A]=32,g[A+1|0]=0}a=589864;break e}if(!((0|(r=e[o+2156>>2]))!=46|e[o+2152>>2]!=46)){r:{s:{i:{if(!(r=e[33285])){if(r=e[33283],e[r>>2]==e[r+4>>2])break r;if(!(r=e[33285]))break i}e[33285]=0;break s}e[33284]=e[33284]+1,r=e[33283],r=0|ct[e[r+8>>2]](r)}if(c=r,(0|r)==46)for(e[o+2152>>2]=32,e[o+2156>>2]=8230,c=e[33285];;){s:{i:{if(!c){if(r=e[33283],e[r>>2]==e[r+4>>2]){c=46;break r}if(!(c=e[33285]))break i}e[33285]=0,r=0;break s}e[33284]=e[33284]+1,r=e[33283],c=0|ct[e[r+8>>2]](r),r=e[33285]}if((0|c)!=46)break r;e[o+2152>>2]=32,e[o+2156>>2]=8230,c=r}}(0|(r=e[o+2156>>2]))!=8230?e[33285]=c:(e[o+2152>>2]=c,r=8230)}if(zA=0,(0|(L=Ls(r)))!=16384){r:if(536621&L)for(r=e[33285];;){if(!r&&(r=e[33283],e[r>>2]==e[r+4>>2])||!(536621&Ls(e[o+2152>>2])))break r;(c=e[33285])?(e[33285]=0,r=0):(e[33284]=e[33284]+1,r=e[33283],c=0|ct[e[r+8>>2]](r),r=e[33285]),e[o+2152>>2]=c}if(1048576&L){e[f+780>>2]=L>>>12&15,at=1,m=0,a=A;continue}(Or(e[o+2152>>2])|32768&L||li(e[o+2152>>2])||(0|(r=e[o+2152>>2]))==63||(i=0,e[33285]||(r=e[33283],i=e[r>>2]==e[r+4>>2],r=e[o+2152>>2]),i||(0|r)==1))&&(zA=1)}if((0|(r=e[o+2156>>2]))==57404&&(e[o+2156>>2]=60,r=60),e[47201]){a=0;r:if(!(1<<(i=Wa(r))&1879048255&&i>>>0<=30)){if(!((i=Wa(r))>>>0>27)){if(116672&(m=1<<i))break r;if(134227968&m){a=!(1024&Se(r,i));break r}}a=1}if(!(l[134772]|!a)&&(e[47201]==1||Lr(188832,e[o+2156>>2]))){e[Z+288>>2]=0,s=e[o+2156>>2],g[o+2336|0]=0,m=e[o+2152>>2],r=0;r:{s:if(!((0|(a=e[34064]))<=0))for(;;){if((0|s)==e[136272+(r<<4)>>2]){if(e[136276+(r<<4)>>2])break r;if($s(0,r))break s;break r}if((0|a)==(0|(r=r+1|0)))break}r=-1}r:if((0|r)>=0)e[o>>2]=r,DA(o+2336|0,86007,o),e[33285]=m;else if((i=!zA)|(0|s)!=46|(0|m)==46||(e[o+2600>>2]=0,e[o+2604>>2]=0,e[o+2540>>2]=86036,!kr(Z,o+2540|0,o+2544|0,o+2600|0,0,0))?r=ei(o+2240|0,Z,s,0):($t(Z,r=o+2544|0,o+2600|0,-1,0),JA(a=r,r=o+2160|0),e[o+64>>2]=r,DA(r=o+2304|0,85451,o- -64|0)),a=r,i|!e[o+2148>>2]|2&l[Z+76|0]){for(r=e[33285],i=1;;){s:{i:{if(!r){if(r=e[33283],e[r>>2]==e[r+4>>2]|(0|s)==60|(0|s)!=(0|m))break s;if(i=i+1|0,m=e[33285])break i;e[33284]=e[33284]+1,r=e[33283],m=0|ct[e[r+8>>2]](r),r=e[33285];continue}if((0|s)==60|(0|s)!=(0|m))break s;m=r,i=i+1|0}r=0,e[33285]=0;continue}break}if(e[o+2152>>2]=m,zA&&(e[33285]=m),(0|i)==1){e[o+16>>2]=a,DA(o+2336|0,86219,o+16|0);break r}if((0|i)<=3){if(g[o+2336|0]=0,(0|(r=e[50786]))<=299&&(e[o+2336>>2]=l[86728]|l[86729]<<8|l[86730]<<16|l[86731]<<24,b[o+2340>>1]=l[86732]|l[86733]<<8),(0|i)>0){for(;e[o+32>>2]=a,DA(r=o+2160|0,86219,o+32|0),m=i>>>0>1,sa(o+2336|0,r),i=i-1|0,m;);r=e[50786]}if((0|r)>299)break r;e[o+2160>>2]=l[86857]|l[86858]<<8|l[86859]<<16|l[86860]<<24,r=l[86860]|l[86861]<<8|l[86862]<<16|l[86863]<<24,g[o+2163|0]=r,g[o+2164|0]=r>>>8,g[o+2165|0]=r>>>16,g[o+2166|0]=r>>>24,sa(o+2336|0,o+2160|0);break r}e[o+56>>2]=a,e[o+52>>2]=i,e[o+48>>2]=a,DA(o+2336|0,86932,o+48|0)}else e[33691]=s,e[33285]=m,b[o+2336>>1]=32;if(i=HA(a=o+2336|0),qA((r=e[o+2148>>2])+189424|0,a),e[o+2148>>2]=r+i,zA){if((0|s)==45){a=16384;break e}if(a=Ls(s),!(2&l[Z+76|0]|(0|r)<=0)){a=(-32769&a)==266270?262148:(28672&a)==4096?266244:262148;break e}if(!(524288&a)){a=(28672&a)==4096?266244:262148;break e}if((0|a)>=0)break e}s=e[o+2156>>2]}}if(s|!(2097152&L)||(r=qA(e[o+2148>>2]+189424|0,ei(o+2336|0,Z,e[o+2156>>2],1)),l[0|r]?(e[o+2148>>2]=e[o+2148>>2]+HA(r),L&=-28673,s=e[o+2156>>2]):s=0),m=0,zA){r:if(Or(c=e[o+2152>>2]))for(i=e[33285],r=0;;){if(!i&&(a=e[33283],e[a>>2]==e[a+4>>2])||!Or(c))break r;r=((0|c)==10)+r|0,(c=e[33285])?(e[33285]=0,i=0):(e[33284]=e[33284]+1,a=e[33283],c=0|ct[e[a+8>>2]](a),i=e[33285])}else r=0;a=(0|(m=e[o+2156>>2]))==46&&(0|r)<2?4194304|L:L;r:{if(!r){i=1,(0|m)!=44|(0|Ke)!=46|e[Z+212>>2]!=26741|We-48>>>0>=10||c-48>>>0>=10&&!ms(c)||(e[o+2156>>2]=1367,i=0),(0|(L=e[o+2156>>2]))!=46|(0|c)!=39||(m=i,L=e[33283],(0|(Qe=e[L>>2]))==e[L+4>>2]?i=0:(i=0|ct[e[L+8>>2]](L),e[L>>2]=Qe),L=e[o+2156>>2],i=(0|i)!=115&m);s:if((0|L)!=46)i&=ce;else{i:if(1&g[Z+106|0]){if(!(Ke-48>>>0<10)&&((m=Ke-73|0)>>>0>15|!(1<<m&40969)||!(1<<(m=We-73|0)&40969&&m>>>0<=15||Or(We))))break i;i=Ke-48>>>0>=10?0:!ms(c)&(0|c)!=45&i}if(ms(c)&&(i=l[Z+208|0]!=0&i),ce){L=e[o+2156>>2];break s}L=32,e[o+2156>>2]=32,i=0}if(!(!i|(0|L)!=46|!e[47203]|(0|c)!=60)){Er=e[o+2148>>2],A=a;break r}if(!i)break r}if(A=e[o+2148>>2]+189424|0,g[0|A]=32,g[A+1|0]=0,e[33285]=c,Ke-48>>>0<10&&(a=rr(c)?a:-4194305&a),(0|r)<2)break e;a=(0|a)==536621?536656:(0|a)==532520?532555:524358;break e}(e[33285]||(m=0,r=e[33283],e[r>>2]!=e[r+4>>2]))&&(m=0,Or(e[o+2152>>2])&&(e[33285]=c))}a=A}if(e[33712]!=1){if((0|s)!=(0|(r=e[o+2156>>2])))i=e[o+2148>>2],(0|r)==57404&&(r=60,e[o+2156>>2]=60);else{A:{if(li(s))r=57384;else{if(r=45,e[o+2156>>2]==45)break A;r=32}e[o+2156>>2]=r}i=e[o+2148>>2]}e[o+2148>>2]=zr(r,i+189424|0)+e[o+2148>>2],Or(e[o+2156>>2])||li(e[o+2156>>2])||(A=e[o+2148>>2],b[MA+(A<<1)>>1]=e[33284]-e[47353],(0|A)<=(i+1|0)||jA(Di+(i<<1)|0,255,A+~i<<1)),r=e[o+2148>>2],e[Wr>>2]=r;A:{r:{if((0|r)>725){if(!rr(e[o+2156>>2]))break r;r=e[o+2148>>2]}if((0|r)<796)continue;break A}if(r=e[o+2148>>2],!(e[o+2156>>2]-48>>>0>=10)&&(0|r)<796)continue}break}}g[0|(A=r+189424|0)]=32,g[A+1|0]=0,e[33285]=e[o+2152>>2],a=16384}J=o+2608|0,at=a,na&&(A=e[f+780>>2],e[na>>2]=A||at>>>12&7),A=(f+5184|0)+(e[f+6800>>2]<<1)|0,b[A+6>>1]=0,b[A+2>>1]=0,b[A+4>>1]=32767,L=z(4095&at,8388608&at?320:10),a=189424;e:{A:{r:if(A=l[189424]){for(;;){if(!!(255&(A=A<<24>>24))&A>>>0<33){if(A=l[0|(a=a+1|0)])continue;break r}break}if(l[0|a])break A}L=(0|(r=L-(A=e[47566])|0))>0?r:0,e[47566]=L+A,at=l[190268]?524288|at:at,e[Z+8240>>2]=at;break e}e[47566]=L,A=l[190268],e[Z+8240>>2]=at,A&&(dt=1,e[47568]=e[47568]+1,(0|(A=e[47569]))<=0||(A=A-1|0,e[47569]=A,A||(g[190280]=0)))}e[49572]=1,e[47572]=655360,e[47573]=0,e[Z+8184>>2]=0,e[Z+8188>>2]=0,A=0,e[Z+288>>2]=0,e[(r=Z- -8192|0)>>2]=0,e[r+4>>2]=0,e[Z+8200>>2]=0,e[Z+8224>>2]=0,e[Z+8228>>2]=0,e[(r=Z+8232|0)>>2]=0,e[r+4>>2]=0,g[f+786|0]=32,b[f+784>>1]=8192,e[f+6812>>2]=32,b[f+1588>>1]=3,e[f+1584>>2]=0,a=0;e:if(!((0|(r=e[f+6800>>2]))<=0)){for(;;){if(b[(f+5184|0)+(a<<1)>>1]>0)break e;if((0|r)==(0|(a=a+1|0)))break}a=r}if(r=M[(f+5184|0)+(a<<1)>>1],b[f+1592>>1]=r,r)for(;A=!!(65535&~r)+A|0,r=M[(f+5184|0)+((a=a+1|0)<<1)>>1];);for(g[f+1594|0]=A,i=3,MA=1,a=0;;){We=e[f+6808>>2],Ma(f+6808|0,(rA=(f+784|0)+i|0)-1|0),!l[Z+170|0]|e[f+6808>>2]-48>>>0>=10||rr(We)&&(e[f+6808>>2]=97),he?e[f+6812>>2]=he:I&&Ma(f+6812|0,I+189423|0),r=I;e:{A:if(a||(r=Gt(f+6816|0,I+189424|0)+I|0,a=e[f+6816>>2])){if(ce=Gt(f+6804|0,A=r+189424|0),(0|a)==1){if(s=r-1|0,he=32,Ke=0,e[f+6812>>2]!=32){r=s,A=32;break A}I=0,a=r;r:{s:switch(l[0|A]-43|0){case 0:a=r+1|0,I=64;break r;case 2:break s;default:break r}a=r+1|0,I=96}if(g[0|(A=a+189424|0)]-48>>>0>=10)c=a+1|0,ce=-1;else{for(ce=Vs(A);a=(A=a)+1|0,g[A+189424|0]-48>>>0<10;);c=a,a=A}if((0|(he=e[47350]))>247)A=0;else if(A=0,!((0|(a=g[a+189424|0]))<0)&&(a=ri(84868,255&a,14))){r=1+(A=a-84868|0)|0,(0|ce)==-1&&(ce=e[105536+(r<<2)>>2],I=0);r:{s:switch(A-8|0){case 0:e[49574]=0,e[49573]=ce;break r;case 4:break s;default:break r}g[199304]=(0|ce)>=3?1:0}A=1,e[47350]=he+1,e[198304+(he<<2)>>2]=(r+I|0)+(ce<<8),r=c}he=e[f+6812>>2],jA(s+189424|0,32,r-s|0),Xr=A+Xr|0,a=0;break e}he=0,(0|a)==32|e[49573]!=36?(Ke=0,A=a):(e[f+6812>>2]!=32|e[f+6804>>2]!=32||(e[49573]=20),Ke=0,A=yi(a,Z))}else e[f+6804>>2]=32,Ke=1,he=0,ce=0,A=32;A:if(hr){if(hr=1,MA=8,a=0,(0|A)!=93|e[f+6804>>2]!=93)break A;r=r+1|0,A=32,hr=0}else if((240&(a=e[49573]))!=64)if(hr=0,16&a)a=0;else{r:{s:{i:{c:{if(!((0|(c=(0|A)==8242||(0|A)==8217||(0|A)==146||(0|A)==180?39:A))!=8216&(0|c)!=63))if(rr(e[f+6808>>2])){if(c=A,rr(e[f+6804>>2])){c=39;break c}}else c=A;o:{if((0|c)!=1367){if((0|c)==1328){T|=1024,c=32;break c}if((A=c-44032|0)>>>0>11183)break c;if(s=((a=((m=65535&A)>>>0)/28|0)>>>0)%21|0,A=A-z(a,28)&65535,c-50500>>>0>587)break o;a=A?A+4519|0:0,s=s+4449|0;break s}T|=131072,Er=e[f+6804>>2],A=e[f+6812>>2],c=32;break i}a=50500+(A+z(s,28)|0)|0,s=(m>>>0)/588|4352;break s}if(Er=e[f+6804>>2],A=e[f+6812>>2],!((a=c-12592|0)>>>0>51)){s=4352|l[a+103296|0],a=0;break s}}o=r+189424|0;i:if(!((0|(a=e[Z+212>>2]))!=28268&(0|a)!=24934|(0|c)!=39)&&!is(A)&&(Gt(f+6820|0,o+1|0),oi(e[f+6820>>2]))){s=601,a=0;c:switch(Er-110|0){case 6:break s;case 0:break c;default:break i}if(e[Z+212>>2]!=24934)break s;g[0|o]=32;break s}if(e[f+6824>>2]=32,(0|(A=e[49897]))>0)e[49897]=A-1,a=0;else{if(!c){a=0,s=0;break r}i:{c:{o:{u:if((zA=e[Z+180>>2])&&(m=c,(Qe=hs(c))&&(m=yi(c,Z)),!Hn(zA)))for(;;){e[f+16>>2]=0,e[f+624>>2]=m,A=Gt(f+16|0,zA)+zA|0;l:if(e[f+624>>2]==e[f+16>>2]){if(l[0|A]){for(s=1,er=0,a=o;pr=Gt(f+16|0,A),yr=Gt(f+624|0,a),Wr=yi(e[f+624>>2],Z),e[f+624>>2]=Wr,a=a+yr|0,er=(yr=(0|Wr)==e[f+16>>2])+er|0,s&=yr,l[0|(A=A+pr|0)];);if(!s)break l;e[49897]=er}if(!(A=A+1|0))break u;if(8&l[188788]&&(e[f>>2]=zA,e[f+4>>2]=A,fr(e[47195],85187,f)),A=Gt(f+6828|0,A)+A|0,l[0|A])break o;a=0;break c}for(;a=A,A=A+1|0,l[0|a];);for(;l[0|(a=(A=a)+1|0)];);if(Hn(zA=A+2|0))break}a=0,s=c;break i}Gt(f+6824|0,A),Qe&&hs(Er)&&(e[f+6824>>2]=vn(e[f+6824>>2])),a=e[f+6824>>2]}s=e[f+6828>>2],T|=2097152,Qe&&(s=vn(s))}if((0|s)!=8)break s}s=r;break e}a?e[f+6804>>2]=a:a=0}rr(s)||oi(s)||Lr(e[Z+336>>2],s)||!rr(e[f+6808>>2])|!(!l[Z+170|0]|s-48>>>0>=10)&e[f+6804>>2]-48>>>0>=10||(s=32,cA=1);r:{s:{i:{c:{o:{if(e[f+6808>>2]-48>>>0<10){if(s-48>>>0<10){A=E;break o}if(1<<(A=s-32|0)&20481&&A>>>0<=14)break c;cA=1}else if(A=0,e[f+6812>>2]!=44||(A=E,(0|s)!=44))break o;s=32;break c}if((0|s)==91&&((0|(c=e[f+6804>>2]))==2||(s=91,(0|c)==91&&e[47204])))break i;E=A}if(rr(s)){c:{o:{u:{if(rr(e[f+6808>>2])){if(!l[Z+171|0]||(A=e[f+6808>>2],!((0|s)>12352)&&(0|A)<12353))break u}else A=e[f+6808>>2];if(Fr=Lr(e[Z+336>>2],A)?Fr:0,(0|(A=e[f+6808>>2]))!=32&&!Lr(e[Z+336>>2],A)){A=32,ee=li(e[f+6808>>2])?ee:256|ee;break o}T=hs(s)?2|T:T,e[f+6808>>2]!=32|g[rA-2|0]-48>>>0>=10|e[f+6812>>2]-48>>>0<10||(g[(f+784|0)+i|0]=32,A=1588+(z(et,12)+f|0)|0,b[A>>1]=M[A>>1]+1,i=i+1|0)}if(A=32,(0|s)==32)break c;if(Fr=Fr+1|0,(0|(c=e[Z+600>>2]))<=0){A=s;break c}if(!((0|s)<=591&(0|(m=e[f+6808>>2]))>=(0|c))){if((0|s)<(0|c)){A=s;break c}if((0|Fr)<2){A=s;break c}if(!((0|m)<=591)){A=s;break c}}if(!rr(m)){A=s;break c}T|=16384,ee|=128}cA=1}if(es=es+1|0,hs(A)){if(c=yi(A,Z),e[Z- -64>>2]){A=ns?c:712,a=ns?a:c,ns=1;break A}if(ms(e[f+6812>>2])){if(e[f+6808>>2]==32){A=c;break A}if(A=32,e[Z+212>>2]!=26465)break r;for(s=85240,m=(f+784|0)+i|0,ce=0;;){if(o=HA(s),l[0|(We=m-o|0)]==32&&!xa(We+1|0,s,o=o-1|0)){if((0|(s=g[s+o|0]))==(0|c)){A=c;break A}if((0|s)==65&&d0(Z,c)){A=c;break A}}if(s=e[131184+((ce=ce+1|0)<<2)>>2],(0|ce)==11)break}break r}if(A=32,(0|c)==32)break A;if(!hs(e[f+6812>>2])){A=c;break A}if(!ms(e[f+6804>>2])){A=c;break A}if(Gt(f+16|0,189424+(r+ce|0)|0),!(e[Z+212>>2]!=28268|(0|Fr)!=2|(0|c)!=106|e[f+6812>>2]!=73)){A=c;break A}if(e[f+6808>>2]==32){A=c;break A}if(!rr(e[f+16>>2])){A=c;break A}ee|=256,he=32,cA=1;break A}if(!MA){MA=0;break A}if((0|Fr)<3){MA=0;break A}if((0|A)!=115){MA=0;break A}if(e[Z+212>>2]!=25966){MA=0;break A}if(e[f+6804>>2]!=32){MA=0;break A}if(MA|=4,A=32,l[783+(s=i+f|0)|0]!=39)break A;g[s+783|0]=32;break A}A=32;c:{o:{u:{l:{switch(s-39|0){default:if((0|s)==95)break A;case 1:case 2:case 3:case 4:case 5:if(s-48>>>0>=10||l[Z+170|0]&&rr(e[f+6808>>2])&&!((c=e[f+6804>>2])-48>>>0<10|c-2406>>>0<10))break c;if((0|(m=e[f+6808>>2]))==32)break o;if(c=e[f+6808>>2],m-48>>>0<10)break u;if((0|(m=c))==(0|(c=e[Z+128>>2])))break l;cA=1;break A;case 6:if(!oi(e[f+6812>>2])&&rr(e[f+6804>>2])){if(e[f+6808>>2]!=32){cA=1;break A}if(T|=128,(0|et)<=0)break A;s=1572+(z(et,12)+f|0)|0,e[s>>2]=16384|e[s>>2];break A}if(s=e[f+6804>>2],!(e[f+6812>>2]!=32|(0|s)!=32)){Zt=4;break A}if((0|s)==45){r=r+1|0,Zt=4;break A}if(A=45,e[f+6808>>2]!=32||!rr(We)||rr(e[f+6812>>2]))break A;g[(f+784|0)+i|0]=32,s=1588+(z(et,12)+f|0)|0,b[s>>1]=M[s>>1]+1,i=i+1|0;break A;case 7:if(e[f+6808>>2]==46){cA=1;break A}if(A=46,(0|et)<=0||(s=1572+(z(et,12)+f|0)|0,1&g[s+1|0])||!rr(e[f+6812>>2]))break A;e[s>>2]=65536|e[s>>2],A=(A=oi(e[f+6804>>2]))||e[f+6804>>2]==45?32:46;break A;case 0:}n:{if((0|(c=e[f+6812>>2]))!=46||(s=115,e[f+6804>>2]!=115)){if(!Vi(c))break n;s=e[f+6804>>2]}if(rr(s))break s}if(1&(s=e[Z+88>>2])){if(rr(e[f+6804>>2]))break s;s=e[Z+88>>2]}if(2&s&&rr(e[f+6812>>2]))break s;if(!(!Lr(e[Z+332>>2],e[f+6812>>2])|(0|We)!=32)){r=(e[f+6804>>2]==32)+r|0;break s}if(c=(0|(s=e[f+6808>>2]))!=115|os,os=0,!(1&c))break A;os=!!(0|oi(s)),Zt=4;break A}if((0|c)==44&E){cA=1;break A}E=1;break c}if((0|c)!=32)break c}rr(We)&&(rr(e[f+6812>>2])||(g[(f+784|0)+i|0]=32,A=1588+(z(et,12)+f|0)|0,b[A>>1]=M[A>>1]+1,i=i+1|0))}A=s;break A}hr=1,s=r+1|0,E=A;break e}A=39,os=0;break A}cA=1,he=32}else{if(A-48>>>0<10){a=0,s=(0|(c=e[49574]+1|0))>(15&e[49573]),e[49574]=s?0:c,A=s?32:A,cA|=s,hr=0;break A}a=0,e[49574]=0,A=(s=e[f+6808>>2]-48>>>0<10)?32:A,cA|=s,hr=0}if(oi(A)){if(e[f+6808>>2]==32){T|=262144,s=r;break e}if(s=e[f+6816>>2]-9>>>0<2,m=1&cA){ce=0;A:if(!((0|p)>(0|(A=r-1|0))))for(;;){if(!(c=b[(f+5184|0)+(A<<1)>>1]))break A;if(ce=((0|c)>0)+ce|0,!((0|p)<=(0|(A=A-1|0))))break}g[1594+(z(et,12)+f|0)|0]=ce}if(ee=s?262144|ee:ee,g[(f+784|0)+i|0]=32,A=i+1|0,!((0|et)>298||(s=(f+1584|0)+z(et,12)|0,(0|(c=M[s+4>>1]))>=(0|A)))){if((0|Xr)<=0?p=e[s>>2]:(p=198300+(e[47350]<<2)|0,e[p>>2]=128|e[p>>2],Xr=0,p=64|e[s>>2]),o=e[47352],g[s+6|0]=o,e[s>>2]=p|(es?MA:-2&MA)|(l[199304]?2048:0)|T,(0|o)>0){for(;T=(p=f+784|0)+A|0,p=p+(A=A-1|0)|0,g[0|T]=l[0|p],(0|A)>(0|c););g[0|p]=32,b[s+4>>1]=c+1,A=i+2|0}c=(f+1584|0)+z(et=et+1|0,12)|0,e[c>>2]=0,b[c+4>>1]=A,i=r;A:if(!((0|(s=e[f+6800>>2]))<=(0|r))){for(;;){if(b[(f+5184|0)+(i<<1)>>1]>0)break A;if((0|s)==(0|(i=i+1|0)))break}i=s}if(ce=M[(f+5184|0)+(i<<1)>>1],b[c+8>>1]=ce,es=0,s=0,ce)for(;s=!!(65535&~ce)+s|0,ce=M[(f+5184|0)+((i=i+1|0)<<1)>>1];);g[c+10|0]=s,e[47352]=0,MA=1,T=ee,ee=0,ns=0}cA=0,a=m?0:a,s=m?I:r}else(0|i)>795?(s=r,r=p,A=i):(A=zr(A,(f+784|0)+i|0)+i|0,s=r,r=p);e[47352]<(0|Zt)&&(e[47352]=Zt),Zt=0,p=r,i=A}if(Ke||(I=s,!((0|i)<799)))break}(0|Xr)<=0|et||(A=198300+(e[47350]<<2)|0,e[A>>2]=128|e[A>>2],e[f+1584>>2]=64|e[f+1584>>2],et=1),A=(f+784|0)+i|0,e[Z+8204>>2]=A-1,r=0,g[0|A]=0,g[f+1590|0]=0,g[1590+(z(et,12)+f|0)|0]=8;e:if((0|et)<=0)e[f+1584>>2]=512|e[f+1584>>2],i=e[49572];else{A=et-1|0;A:if((0|et)!=1)for(a=A;;){if(!li(g[M[1588+(z(a,12)+f|0)>>1]+(f+784|0)|0])){r=a;break A}if(c=(0|a)>1,a=a-1|0,!c)break}if(r=(f+1584|0)+z(r,12)|0,e[r>>2]=16|e[r>>2],4194304&at&&(A=(f+1584|0)+z(A,12)|0,256&(r=e[A>>2])||(e[A>>2]=65536|r)),e[f+1584>>2]=512|e[f+1584>>2],!((0|et)<=0|(0|(i=e[49572]))>990))for(c=3|(A=f+624|0),m=2|A,he=f+754|0,Ke=!(4194304&at),I=0,p=0;;){e[47354]=e[47354]+1;A:{if((0|(A=e[49827]))<=0||(A=A-1|0,e[49827]=A,A)){if(l[190280])break A}else g[190280]=0;A=M[1588+(z(I,12)+f|0)>>1]+(f+784|0)|0;r:if(!(g[0|A]-48>>>0>=10)&&(r=f+624|0,a=A,e[Z+112>>2]!=1227133512)){for(;;){s:{if(g[0|a]-48>>>0<10)g[0|r]=l[0|a],r=r+1|0,a=a+1|0;else{if(e[Z+124>>2]!=g[0|a]|l[a+1|0]!=32||(i=a+2|0,l[a+3|0]==32|g[0|i]-48>>>0>=10|l[a+4|0]==32))break s;I=I+1|0,a=i}if(r>>>0<he>>>0)continue;break r}break}jA(A+(r=r-(E=f+624|0)|0)|0,32,(a=(i=a-A|0)-r|0)>>>0<=i>>>0?a:0),wt(A,E,r)}for(r=0;a=r,r=r+1|0,g[A+a|0]-48>>>0<10;);r:if(a-5>>>0<=27){for(g[f+626|0]=32,b[f+624>>1]=8224,l[0|A]!=48&e[Z+132>>2]>=(0|a)||(r=(f+1584|0)+z(I,12)|0,e[r>>2]=524288|e[r>>2]),ee=(f+1584|0)+z(I,12)|0,ce=0,i=c;r=A,!((A=g[0|A])-48>>>0>=10&(0|A)!=e[Z+128>>2])&&(g[0|i]=A,A=i+1|0,E=a,(0|(a=a-1|0))<=0?i=A:e[Z+112>>2]>>>a&1?(o=e[ee+4>>2],T=(f+16|0)+z(ce,12)|0,e[T>>2]=e[ee>>2],e[T+4>>2]=o,e[T+8>>2]=e[ee+8>>2],ce=ce+1|0,(0|(T=e[Z+124>>2]))!=32&&(g[i+1|0]=T,A=i+2|0),g[0|A]=32,i=A+1|0,8&l[ee+2|0]||((Zt=e[Z+112>>2])>>>E-2&1&&(g[A+1|0]=48,g[A+2|0]=48,Zt=e[Z+112>>2],i=A+3|0),Zt>>>E-3&1&&(g[0|i]=48,i=i+1|0))):i=A,A=r+1|0,i>>>0<he>>>0););if(a=e[ee+4>>2],A=(f+16|0)+z(ce,12)|0,e[A>>2]=e[ee>>2],e[A+4>>2]=a,a=e[ee+20>>2],e[A+16>>2]=e[ee+16>>2],e[A+20>>2]=a,a=e[ee+12>>2],e[A+8>>2]=e[ee+8>>2],e[A+12>>2]=a,a=1,(0|ce)>0)for(;A=(f+16|0)+z(a,12)|0,e[A>>2]=-262209&e[A>>2],(0|ce)>=(0|(a=a+1|0)););if(A=l[r+4|0]|l[r+5|0]<<8|l[r+6|0]<<16|l[r+7|0]<<24,a=l[0|r]|l[r+1|0]<<8|l[r+2|0]<<16|l[r+3|0]<<24,g[0|i]=a,g[i+1|0]=a>>>8,g[i+2|0]=a>>>16,g[i+3|0]=a>>>24,g[i+4|0]=A,g[i+5|0]=A>>>8,g[i+6|0]=A>>>16,g[i+7|0]=A>>>24,A=l[r+12|0]|l[r+13|0]<<8|l[r+14|0]<<16|l[r+15|0]<<24,r=l[r+8|0]|l[r+9|0]<<8|l[r+10|0]<<16|l[r+11|0]<<24,g[i+8|0]=r,g[i+9|0]=r>>>8,g[i+10|0]=r>>>16,g[i+11|0]=r>>>24,g[i+12|0]=A,g[i+13|0]=A>>>8,g[i+14|0]=A>>>16,g[i+15|0]=A>>>24,g[i+16|0]=0,i>>>0<=c>>>0)break r;for(A=l[ee+6|0],ce=0,a=c;;){for(p=bt(Z,a,(f+16|0)+z(ce,12)|0,255&A);A=l[0|a],a=a+1|0,(0|A)!=32;);if(A=0,g[ee+6|0]=0,ce=ce+1|0,!(a>>>0<i>>>0))break}}else{if(e[47352]=0,p=bt(Z,A,r=(f+1584|0)+z(I,12)|0,l[r+6|0]),(0|(a=e[47352]))>l[r+18|0]&&(g[r+18|0]=a,e[47352]=0),!(!(4096&p)|l[0|A]==32))for(;jA(f+624|0,0,150),e[f+624>>2]=538976288,e[f+628>>2]=538976288,g[f+632|0]=32,bt(Z,wt(m,A,a=Gt(f+16|0,A)),r,0),l[0|(A=A+a|0)]!=32;);50331648&p&&(L=(A=Ke|(~e[33264]+et|0)!=(0|I))?L:10,A|!na||(e[na>>2]=4,L=10))}if(128&p&&!((0|(r=e[33264]))<=0)){if(A=0,a=r,i=3&r)for(;E=(f+1584|0)+z(a+I|0,12)|0,e[E>>2]=1048576|e[E>>2],a=a-1|0,(0|i)!=(0|(A=A+1|0)););if(r>>>0>=4)for(;A=(f+1584|0)+z(a+I|0,12)|0,e[A>>2]=1048576|e[A>>2],e[(r=A-12|0)>>2]=1048576|e[r>>2],e[(r=A-24|0)>>2]=1048576|e[r>>2],e[(A=A-36|0)>>2]=1048576|e[A>>2],a=a-4|0;);e[33264]=a}}if(i=e[49572],(0|et)<=(0|(I=I+1|0)))break e;if(!((0|i)<991))break}}if((0|(a=e[47351]))<(0|(c=e[47350]))){for(he=e[47202],Ke=e[49846],ce=e[47352];;){A=(r=e[198304+(a<<2)>>2])>>8;e:{A:switch((31&r)-9|0){case 0:he=A;break e;case 4:Ke=A;break e;case 3:break A;default:break e}ce=r>>>0>=256?A+ce|0:0}if(!(!(128&r)&(0|c)>(0|(a=a+1|0))))break}e[47352]=ce,e[47351]=a,e[49846]=Ke,e[47202]=he}e[49572]=i+2,e[(A=190288+(i<<3)|0)>>2]=589824,b[A+4>>1]=s,e[A+8>>2]=589824,b[A+12>>1]=s,r=et&&e[47199]?L:10,e[33285]?A=0:(A=e[33283],A=e[A>>2]==e[A+4>>2]),L=A?r:L,I=dt,c=0,et=0,J=m=J-32192|0,e[m+24>>2]=0,e[m+28>>2]=0,e[m+16>>2]=0,e[m+20>>2]=0,e[m+8>>2]=0,e[m+12>>2]=0,e[m>>2]=0,e[m+4>>2]=0,s=e[49572],o=M[190284+(s<<3)>>1];e:{if((0|(a=s-3|0))<0)A=a;else{for(;;){if(c=(0|(r=127&l[3+(A=190288+(a<<3)|0)|0]))<(0|c)?c:r,M[A+4>>1])A=a;else if(A=-1,r=(0|a)>0,a=a-1|0,r)continue;break}if(c>>>0>3)break e}for(;;){if((0|(A=A-1|0))<0)break e;if(64&l[0|(r=190288+(A<<3)|0)]){g[r+3|0]=4;break e}if(!(l[r+3|0]<4))break}}if(A=e[Z+292>>2],a=0,(0|s)<=0)c=0;else for(E=-1,c=0;;){r=A,e[Z+292>>2]!=(0|A)&&(b[(A=190288+(a<<3)|0)>>1]=32|M[A>>1]),(0|c)>0&&(ee=e[4+(p=190288+(a<<3)|0)>>2],e[(i=190288+(A=a-c<<3)|0)>>2]=e[p>>2],e[i+4>>2]=ee,(0|E)!=-1&&(b[4+(A+190288|0)>>1]=E),E=-1);e:{if(l[2+(190288+(i=a<<3)|0)|0]==21){if(A=l[7+(p=i+190288|0)|0],2&l[0|p])break e;A:if((0|A)!=(0|r)){if(p=l[10+(i+190288|0)|0]-9|0){if((0|p)==12)break A;break e}if(l[18+(i+190288|0)|0]!=21)break e}(0|E)==-1&&(E=(A=M[4+(i+190288|0)>>1])||-1),c=c+1|0}A=r}if((0|s)==(0|(a=a+1|0)))break}if(e[49572]=s-c,ha(A),(r=e[Z+36>>2])&&!((0|(A=(c=e[49572])-1|0))<0))for(he=256&r,T=4&r,Ke=8&r,ee=15&r,cA=16&r,dt=2&r,r=r>>>8&1,a=0;;){if(i=c,s=a,c=A,(0|(A=l[2+(p=190288+(MA=A<<3)|0)|0]))==21){e:{A:{if((0|(a=i-2|0))>=0)for(;;){if(l[2+(190288+(A=a<<3)|0)|0]==21)break A;if(A=(0|a)>0,a=a-1|0,!A)break}A=e[Z+292>>2];break e}A=l[7+(A+190288|0)|0]}ha(A),A=l[p+2|0]}if(a=s,(A=e[144464+((255&A)<<2)>>2])&&(a=r,!(32&l[0|p]))){a=l[A+11|0],E=0,dt&&((0|(i=l[0|A]))!=118&(0|i)!=82||(s=cA?0:s,E=1));e:{A:{r:{s:switch((i=253&a)-4|0){case 1:break r;case 0:break s;default:break A}if(ee&&(a=1,!s)||(0|(a=s))!=2||(a=2,!(A=l[A+13|0])))break e;g[p+2|0]=A;break e}if(ee&&(a=2,!s)||(0|(a=s))!=1||(a=1,!(A=l[A+13|0])))break e;g[p+2|0]=A;break e}a=0,Ke&&(a=i?s:0)}a=A=E?0:a,M[4+(MA+190288|0)>>1]&&(a=A=T?0:A,he&&(a=A||1))}if(!((0|(A=c-1|0))>=0))break}if(ha(e[Z+292>>2]),e[49572]<=0)cA=-2,a=0;else{for(a=-1,s=0,p=0,Ke=0;;){A=p<<3,(0|a)!=-1&&(b[4+(A+190288|0)>>1]=a),l[2+(E=A+190288|0)|0]==21&&ha(l[7+(A+190288|0)|0]),ee=e[49572];e:{if(!(32&l[0|(i=A+190288|0)])){s=(0|(r=ee-1|0))>(0|p)?e[144464+(l[10+(A+190288|0)|0]<<2)>>2]:s,!(M[i+12>>1]|(0|r)==(0|p))&&(ce=0,l[s+11|0]|!s)||(ce=1),c=l[E+2|0];A:if(!((0|(cA=e[49848]))<=0))for(r=A+190288|0,a=0;;){if(he=z(a,3),l[he+199408|0]==(255&c)&&!((T=l[2+(he+199408|0)|0])&(1^ce)|(4&l[r+3|0]?2&T:0)|(M[r+4>>1]?0:4&T))){if(c=l[1+(he+199408|0)|0],g[E+2|0]=c,!(2&l[e[144464+(c<<2)>>2]+4|0])|l[r+3|0]<2)break A;g[r+3|0]=0;break A}if((0|cA)==(0|(a=a+1|0)))break}if(!(255&c)){a=M[4+(A+190288|0)>>1];break e}}a=e[i+4>>2],A=(m+32|0)+(Ke<<5)|0,r=e[i>>2],e[A>>2]=r,e[A+4>>2]=a,r=e[144464+(r>>>14&1020)>>2],e[A+8>>2]=r,g[A+17|0]=l[r+11|0],Ke=Ke+1|0,a=-1}if(!((0|Ke)<1e3&(0|ee)>(0|(p=p+1|0))))break}if(c=0,a=0,!((0|(cA=Ke-2|0))<=0))for(;;){e:if(M[4+((m+32|0)+(c<<5)|0)>>1]){for(r=(0|c)>(0|cA)?c:cA,a=0,A=c;;){if((0|A)!=(0|r)){if(a=(0|a)>(0|(i=l[3+((s=m+32|0)+(A<<5)|0)|0]))?a:i,!M[4+(s+((A=A+1|0)<<5)|0)>>1])continue}else A=r;break}if((0|A)<=(0|c))break e;if(r=~c+A|0,s=0,i=A-c&7)for(;g[6+((m+32|0)+(c<<5)|0)|0]=a,c=c+1|0,(0|i)!=(0|(s=s+1|0)););if(r>>>0<7)break e;for(;g[6+(r=(m+32|0)+(c<<5)|0)|0]=a,g[r+38|0]=a,g[r+70|0]=a,g[r+102|0]=a,g[r+134|0]=a,g[r+166|0]=a,g[r+198|0]=a,g[r+230|0]=a,(0|(c=c+8|0))!=(0|A););}else A=c+1|0;if(c=A,!((0|cA)>(0|A)))break}}for(e[m+40>>2]=e[36125],ha(e[Z+292>>2]),We=(0|a)<4,p=1,he=1,ee=0,r=0,a=0,dt=0;;){e:{A:{r:{s:{if(r){if(s=(A=m+32|0)+((E=a-1|0)<<5)|0,ee=l[2+(A+(a<<5)|0)|0],(0|E)>0){if(A=p-(a=(0|p)>0)|0,E>>>0>=(c=a?p:2)>>>0)for(;i=(a=(m+32|0)+(c<<5)|0)-32|0,p=e[a+12>>2],e[i+8>>2]=e[a+8>>2],e[i+12>>2]=p,p=e[a+4>>2],e[i>>2]=e[a>>2],e[i+4>>2]=p,p=e[a+28>>2],e[i+24>>2]=e[a+24>>2],e[i+28>>2]=p,p=e[a+20>>2],e[i+16>>2]=e[a+16>>2],e[i+20>>2]=p,(0|E)>=(0|(c=c+1|0)););p=A}ce=e[144464+(ee<<2)>>2],e[s>>2]=0,e[s+4>>2]=0,e[s+24>>2]=0,e[s+28>>2]=0,e[s+16>>2]=0,e[s+20>>2]=0,e[s+8>>2]=0,e[s+12>>2]=0,g[s+2|0]=r,A=e[144464+(r<<2)>>2],e[s+8>>2]=A,ee=s}else{if((0|a)>=(0|cA)|(0|dt)>=997)break s;i=l[2+(s=(c=a<<5)+(m+32|0)|0)|0],A=e[144464+(i<<2)>>2],e[s+8>>2]=A,E=M[s+4>>1],(0|i)==21&&ha(l[7+(c+(m+32|0)|0)|0]),p=E?a:p,ce=e[144464+(l[s+34|0]<<2)>>2],e[s+40>>2]=ce,E=a}if(!A){r=0,a=E+1|0;continue}if(tr(Z,256,s,m+32040|0,m),(0|(a=e[m+32052>>2]))>0&&(c=(m+32|0)+(E<<5)|0,ce=e[144464+(a<<2)>>2],e[c+40>>2]=ce,g[c+34|0]=a,g[c+49|0]=l[ce+11|0]),a=0,r)r=A;else if((0|(c=e[m+32056>>2]))<=0)r=A;else{r=e[144464+(c<<2)>>2],e[s+8>>2]=r,a=l[s+2|0],g[s+2|0]=c,c=M[s>>1];i:if(l[r+11|0]!=2)b[s>>1]=65531&c;else{if(b[s>>1]=4|c,l[A+11|0]==2)break i;g[s+3|0]=0}tr(Z,256,s,m+32040|0,m)}if((0|(i=e[m+32048>>2]))<=0)c=r;else{if(c=e[144464+(i<<2)>>2],g[s+2|0]=i,e[s+8>>2]=c,A=l[c+11|0],Ke=1,(0|i)==1){MA=(0|A)==2;break A}i=M[s>>1];i:if((0|A)!=2)b[s>>1]=65531&i;else{if(b[s>>1]=4|i,l[r+11|0]==2)break i;g[s+3|0]=0}tr(Z,256,s,m+32040|0,m)}if(MA=0,(0|(A=l[c+11|0]))!=2){Ke=0;break A}if(MA=1,Ke=0,A=2,l[s+3|0]>1){et=0;break A}i=s+3|0,et=et+1|0,r=s;i:{if(8&(T=e[Z+12>>2])){for(;;){c:switch(T=r,r=r+32|0,l[T+49|0]){case 0:break A;case 2:break c;default:continue}break}if(l[0|(r=T+35|0)]>1)break A;if(l[s+6|0]<=3&&(g[0|i]=0),l[T+38|0]<4)break i;break A}if(1&et|(0|et)<2)break A;if(2&T)break r;if(We)r=i;else if(r=i,M[s+36>>1])break r}g[0|r]=0;break A}e[36423]=dt+2,b[(A=145840+(dt<<5)|0)>>1]=0,g[A+2|0]=9,g[A+20|0]=2,e[A+12>>2]=L,b[A+4>>1]=o,g[A+17|0]=0,g[A+18|0]=0,e[A+8>>2]=e[36125],b[A+32>>1]=0,g[A+34|0]=9,g[A+52|0]=0,e[A+44>>2]=0,b[A+36>>1]=0,g[A+49|0]=0,g[A+50|0]=0,e[A+40>>2]=e[36126],ha(e[Z+292>>2]),J=m+32192|0;break e}et=1}if(!(8&(r=M[s+32>>1]))|(0|E)<=0||(i=l[ce+11|0])>>>0>15|!(1<<i&457)||(a=l[ce+10|0],b[s+32>>1]=8^r),Qe=M[s+36>>1]){A:if(r=e[Z+4>>2]){switch(0|A){default:a=512&r?11:a;break;case 0:break A;case 2:}if(l[ce+11|0]==2){(i=12&r)&&(a=(0|i)!=12?23:11);r:if(MA){switch(3&r){case 2:a=10;break r;case 0:break r}a=23}l[s+35|0]<4||(a=256&r?10:a)}}if(!((0|s)==(0|ee)|(0|dt)<=0)){A:{r:{s:switch(0|(r=7&e[Z>>2])){case 0:break A;case 1:break s;default:break r}if(a-12>>>0>4294967293)break A}a=l[r+101916|0]}a=e[47205]>0?24:a}}if(e[s+72>>2]=e[144464+(l[s+66|0]<<2)>>2],r=e[m+32060>>2],r=a||r||a,!Ke){g[17+(i=145840+(T=dt<<5)|0)|0]=A,e[i+8>>2]=c,g[i+16|0]=0,b[i>>1]=M[s>>1],g[i+3|0]=15&l[s+3|0],g[i+6|0]=l[s+6|0],a=l[s+7|0],b[i+4>>1]=0,g[i+7|0]=a,Ke=l[c+10|0],g[i+2|0]=Ke;A:if(a=M[s+4>>1]){if(b[i+4>>1]=a,I=1&I?5:1,g[20+(s=T+145840|0)|0]=I,a=he,he=0,!a){I=0;break A}g[s+20|0]=8|I,I=0}else g[20+(T+145840|0)|0]=0;e[12+(a=T+145840|0)>>2]=e[m+32084>>2]<<1,!Qe|(0|Ke)!=24||(0|(s=e[47205]))<=0||(e[i+8>>2]=e[36126],e[a+12>>2]=z(s,14)),(1<<A&428?A>>>0<=8:0)|2&l[c+7|0]&&(e[a+12>>2]=128,g[i+16|0]=0),g[21+(A=T+145840|0)|0]=255,g[A+22|0]=255,b[A+18>>1]=5120,dt=dt+1|0}a=E+1|0;continue}break}b[88922]=1,e[44462]=0,Xr&&(b[145776+(e[36423]<<5)>>1]=2,A=198304+(e[47350]<<2)|0,e[A>>2]=128,e[(A=A-4|0)>>2]=128|e[A>>2]),g[190268]=at>>>19&1,Ts&&(e[Ts>>2]=at<<14>>31&189360)}J=f+6832|0,cA=e[47192],MA=e[t+12>>2],r=0,i=0,p=0,T=0,ee=0,Zt=0,L=0,J=o=J-6e3|0;e:if(!((0|(s=(dt=e[36423])-1|0))<=0)){for(;g[2+(o+z(r,6)|0)|0]=0,4&l[145840+(A=r<<5)|0]?(a=o+z(T,6)|0,g[a+1|0]=0,A=A+145840|0,g[a+3|0]=l[A+49|0],A=l[A+3|0],g[0|a]=A,T=T+1|0,Zt=(A>>>0>3)+Zt|0):l[e[8+(A+145840|0)>>2]+10|0]!=27|(0|T)<=0||(A=(o+z(T,6)|0)-4|0,g[0|A]=4|l[0|A]),(0|s)!=(0|(r=r+1|0)););if(g[o+z(T,6)|0]=0,T)if(e[cA+148>>2]==1){if(!((0|dt)<=0)){for(A=-2&dt,a=1&dt,r=145840;p=l[r+17|0]==2&&l[r+3|0]>3?i:p,p=l[r+49|0]==2&&l[r+35|0]>3?1|i:p,r=r- -64|0,i=i+2|0,(0|A)!=(0|(ee=ee+2|0)););!a|l[r+17|0]!=2||(p=l[r+3|0]>3?i:p)}if(g[3+(c=145840+(A=p<<5)|0)|0]=7,e[cA+212>>2]==30313&&(l[7+(A=A+145840|0)|0]||(g[A+7|0]=ra(55),dt=e[36423])),!((0|dt)<=0)){for(ee=0,r=145840,A=145840,i=E=e[36125],s=0,he=1;;){if(l[r+17|0]?I=e[36125]:(I=e[36125],E=(a=l[e[r+8>>2]+14|0]>50)?I:E,he|=a),a=l[r+20|0]?I:i,4&l[0|r]){I=l[r+7|0],i=e[144464+(I<<2)>>2];A:{if((0|(T=e[cA+212>>2]))==6840683){if(e[a>>2]!=49||(T=e[i>>2]-49|0)>>>0>5|!(1<<T&41))break A;g[A+7|0]=ra(50),T=e[cA+212>>2]}if(!((0|T)!=6516078&(0|T)!=31336)){T=0,I||(s=ra(1&(T=s|he)?13621:12593),g[r+7|0]=s,i=e[144464+(s<<2)>>2]),(0|p)!=(0|ee)|(1024|e[i>>2])!=13621||(g[c+3|0]=6),e[E>>2]==3420466&&(g[A+7|0]=ra(e[i>>2]==3420466?13619:12594));r:{if(e[a>>2]==12597){if((0|(I=e[i>>2]))!=12597)break r;g[A+7|0]=ra(13109)}I=e[i>>2]}s=T,(0|I)==12593&&((0|(I=e[E>>2]))==13621&&(g[r+7|0]=ra(12850),I=e[E>>2]),(0|I)==13619&&(g[r+7|0]=ra(13107),I=e[E>>2]),(0|I)==3420466&&(g[r+7|0]=ra(13364)),g[r+3|0]=0)}}he=0,E=i,A=r}else i=a;if(r=r+32|0,!((0|(ee=ee+1|0))<(0|(a=e[36423]))))break}if(i=0,r=145840,!((0|a)<=0))for(;4&l[0|r]&&((A=l[r+7|0])||(g[r+7|0]=17,A=17),A=e[144464+(A<<2)>>2],g[r+21|0]=l[A+12|0],g[r+22|0]=l[A+13|0]),r=r+32|0,(0|a)!=(0|(i=i+1|0)););}}else{if(A=e[cA+152>>2],A=cA+z(We=(0|A)>7?1:A,6)|0,Z=l[0|(We?A+637:cA+157)],at=l[0|(We?636+(A+MA|0):156+(cA+MA|0))],g[133068]=(0|MA)==4,!((0|T)<=0)){for(Ke=T-1|0,ce=MA-1>>>0>1,a=0,s=0;;){f=o+z(s,6)|0,L=((A=l[0|f])<<24>>24>3)+L|0;A:if((0|A)==6){A=s-3|0,r=s;r:{for(;;){if((0|r)<=(0|a)|(0|A)>=(0|r))break r;s:switch(c=o+z(r=r-1|0,6)|0,l[0|c]-4|0){case 2:break r;case 0:break s;default:continue}break}g[0|c]=3}r=s;r:{for(;;){if((0|T)<=(0|(r=r+1|0)))break r;s:switch(l[o+z(r,6)|0]-4|0){case 0:break r;case 2:break s;default:continue}break}g[f+2|0]=2,g[0|f]=5,A=a;break A}if(l[0|f]==6){g[f+2|0]=2,m=0;r:if((0|T)<=(0|(A=s+1|0)))c=s,hr=0;else if(hr=1,(0|(i=g[o+z(A,6)|0]))>4)c=s;else{for(p=(Zt-L|0)>1,c=s;;){if(r=A,(255&i)==4&&(A=p+1|0,p=1,!((0|A)<=1))){A=r;break r}if(hr=(0|T)>(0|(A=r+1|0)),(0|A)==(0|T))break;if(c=r,(0|(i=g[o+z(A,6)|0]))>4)break r}c=Ke,A=T}i=-1,I=0,p=0,he=-1,ee=0,E=-1;r:{if((0|(r=a))<(0|A)){for(;i=(Qe=(0|(E=g[o+z(r,6)|0]))>3)&&(0|i)<0?r-a|0:i,m=(ee=(0|p)>(0|E))?m:(0|p)<(0|E)?r:I,he=Qe?r:he,I=ee?I:r,Qe=(0|r)!=(0|c),p=ee?p:E,r=r+1|0,Qe;);if(ee=I,E=he,(0|i)>=0)break r}i=A,I=ee,he=E}e[33269]=c-I,e[33268]=i,e[33270]=I,e[33271]=m;r:if(l[133068])e[33270]=A,e[33271]=A;else if((0|he)>=0){if((0|A)!=(0|T))break r;g[o+z(he,6)|0]=7}else g[o+z(I,6)|0]=7;Ka(o,We,a,A,at),!hr&!!(0|MA)||(at=ce?l[cA+156|0]:l[cA+157|0])}else A=a}else A=a;if((0|A)>=(0|s))a=A;else if(4&l[f+2|0]){for(a=s+1|0,i=-1,he=0,I=0,p=0,r=A,m=-1;i=(ee=(0|(c=g[o+z(r,6)|0]))>3)&&(0|i)<0?r-A|0:i,he=(E=(0|c)<(0|p))?he:(0|c)>(0|p)?r:I,m=ee?r:m,I=E?I:r,ee=(0|r)!=(0|s),p=E?p:c,r=r+1|0,ee;);e[33269]=s-I,e[33270]=I,e[33271]=he,e[33268]=(0|i)<0?a:i,l[133068]?(e[33270]=a,e[33271]=a):(0|m)>=0?g[o+z(m,6)|0]=7:g[o+z(I,6)|0]=7,Ka(o,We,A,a,Z)}else a=A;if((0|T)==(0|(s=s+1|0)))break}if(!((0|a)>=(0|T))){for(i=-1,he=0,I=0,p=0,r=a,m=-1;i=(c=(0|(A=g[o+z(r,6)|0]))>3)&&(0|i)<0?r-a|0:i,he=(s=(0|A)<(0|p))?he:(0|A)>(0|p)?r:I,m=c?r:m,I=s?I:r,p=s?p:A,(0|T)!=(0|(r=r+1|0)););e[33270]=I,e[33271]=he,e[33269]=~I+T,e[33268]=(0|i)<0?T:i,l[133068]?(e[33270]=T,e[33271]=T):(0|m)>=0?g[o+z(m,6)|0]=7:g[o+z(I,6)|0]=7,Ka(o,We,a,T,at)}}if((0|dt)<=0)break e;for(r=0,ee=0;;){if(E=a=145840+(c=r<<5)|0,A=o+z(ee,6)|0,i=l[0|A],g[a+3|0]=i,4&l[0|a]){s=c+145840|0,a=l[A+4|0],g[s+21|0]=a,p=l[A+5|0],g[s+16|0]=0,g[s+22|0]=p;A:{if(1&(I=l[A+2|0]))A=2;else{if(i>>>0<6)break A;A=l[A+1|0]}g[s+16|0]=A}a>>>0<=(255&p)>>>0?(A=p,p=a):(g[s+21|0]=p,g[s+22|0]=a,A=a),(a=l[7+(c+145840|0)|0])&&(A=(255&A)+(255&p)>>>1|0,a=e[144464+(a<<2)>>2],g[s+22|0]=A+l[a+13|0],g[s+21|0]=A+l[a+12|0]),2&I&&(g[E+3|0]=8|i),ee=ee+1|0}if((0|dt)==(0|(r=r+1|0)))break}}}if(J=o+6e3|0,ee=e[47192],r=0,ce=0,m=0,L=0,J=o=J-160|0,e[36423]>=2)for(MA=e[30450],i=1;;){if(i=(A=i)+1|0,T=l[3+(s=145840+(p=A<<5)|0)|0],2&(I=M[s>>1])){for(;(31&(c=e[198304+(L<<2)>>2]))==2&&(Ss(127&c,c>>>8|0),e[36432]=110,e[36433]=100,e[36434]=450,e[36430]=5,I=e[50786],E=e[32972],(0|(a=e[E+84>>2]))>0&&(I=(0|z(a,I))/100|0),he=l[((0|(a=(0|I)>=359?359:I))<=80?80:a)+101856|0],a=(0|(a=(0|I)>=450?450:I))>399?6:(0|a)>379?7:he,e[32526]=(0|z(a,e[E+72>>2]))/256,e[32527]=(0|z(a,e[E+76>>2]))/256,e[32528]=(0|z(a,e[E+80>>2]))/256,a>>>0>7||(E=a-1|0,e[32528]=E,e[32526]=a,e[32527]=E)),L=L+1|0,!(128&c););I=M[s>>1]}E=i<<5,he=A-1|0,a=7&T;e:{A:{r:{s:{i:{c:{o:{u:{l:{n:switch(at=l[17+(p+145840|0)|0],0|(c=4&I?2:at)){case 2:break i;case 3:case 8:break c;case 5:break o;case 6:case 7:break u;case 4:break l;case 0:break n;default:break e}r=0;break e}if((0|(A=l[17+(145840+(he<<5)|0)|0]))!=6?(a=(0|A)==4?60:e[34063]>0||a>>>0<4?48:60,g[18+(p+145840|0)|0]=a):(a=25,g[18+(p+145840|0)|0]=25),!(16&l[0|ee])|!l[20+(p+145840|0)|0]||(g[18+(p+145840|0)|0]=60,a=60),64&l[e[8+(p+145840|0)>>2]+6|0]&&(a=a+30|0,g[18+(p+145840|0)|0]=a),r=0,!(8&I))break e;g[18+(p+145840|0)|0]=l[ee+164|0]+a;break e}!(s=l[20+(A=p+145840|0)|0])|1&g[e[A+8>>2]+7|0]&l[17+(145840+(he<<5)|0)|0]==2||(g[18+(p+145840|0)|0]=15),a=l[17+(E+145840|0)|0],8&l[e[8+(p+145840|0)>>2]+4|0]|a|l[17+(145840+(he<<5)|0)|0]!=8||(g[18+(p+145840|0)|0]=25),64&l[e[8+(145840+(A=he<<5)|0)>>2]+5|0]&&(g[18+(p+145840|0)|0]=30),!s|!(16&e[ee>>2])||(g[18+(p+145840|0)|0]=30);u:if(l[20+(E+145840|0)|0]|!(32&l[e[8+(p+145840|0)>>2]+4|0])|(0|a)!=4)e[12+(p+145840|0)>>2]=256;else{if(s=p+145840|0,l[17+(A+145840|0)|0]==2){e[s+12>>2]=200;break u}e[s+12>>2]=150}if((0|c)!=7||(ce|=(0|a)==2,(254&l[17+(A+145840|0)|0])!=2))break e;e[12+(p+145840|0)>>2]=e[12+(A+145840|0)>>2]+255>>>1;break e}(254&(a=l[17+(145840+(A=he<<5)|0)|0]))==6|(0|a)==3|32&e[e[8+(A+145840|0)>>2]+4>>2]&&(g[18+(p+145840|0)|0]=30);o:if((254&(s=l[17+(E+145840|0)|0]))==2){ce=l[20+(E+145840|0)|0]&&(0|s)!=2?ce:1,g[18+(s=p+145840|0)|0]=40,T=0;u:{switch(0|a){case 0:if((A=e[12+(A+145840|0)>>2])>>>0>39)break u;T=40-A|0;break u;case 2:break u}if(l[20+(p+145840|0)|0])break o;T=20;l:switch(a-3|0){case 1:if(T=0,!(8&l[e[8+(A+145840|0)>>2]+4|0]))break u;break o;case 0:break u;case 5:break l;default:break o}T=12}g[s+18|0]=T}if(!(16&l[0|ee])|!l[20+(p+145840|0)|0]||l[18+(A=p+145840|0)|0]>19)break e;g[A+18|0]=20;break e}s=l[ee+296|0],c=a=p+145840|0,e[a+12>>2]=256,g[a+19|0]=s;c:if(l[a+20|0]){I=25;o:switch(l[17+(145840+(he<<5)|0)|0]-2|0){case 0:if(I=12,1&g[e[8+(p+145840|0)>>2]+7|0])break c;break;case 1:break o;default:break c}g[18+(p+145840|0)|0]=I}if((0|(E=l[17+(E+145840|0)|0]))==2){m=1;break e}if(g[22+(s=p+145840|0)|0]=r,(254&l[17+(145840+(a=he<<5)|0)|0])==2)break s;if(a=r,(0|(c=e[36423]))<=(0|A))break A;for(;;){if(l[17+(145840+(a=A<<5)|0)|0]==2){a=l[22+(a+145840|0)|0],g[s+22|0]=a;break A}if((0|c)==(0|(A=A+1|0)))break}break r}if(cA=p+145840|0,Ke=a^a>>>0<2,a=(dt=8&T)?25:l[296+(Ke+ee|0)|0]-m|0,g[cA+19|0]=a,(e[36423]-3|0)>(0|A)||(0|(c=255&a))<=(0|(a=e[ee+52>>2]))||(g[cA+19|0]=a),a=0,T=0,!(I=l[s+52|0]))for(;c=e[s+40>>2],T=l[s+49|0]==2?(~e[c+4>>2]>>>20&1)+T|0:T,a=l[c+10|0]==27?2:a,c=s,s=s+32|0,!(I=l[c+84|0]););c=cA+96|0,A=145840+(We=A+2<<5)|0,e[34063]=T,f=l[e[s+40>>2]+10|0],E=E+145840|0,l[17+(We+145840|0)|0]|l[e[E+8>>2]+10|0]!=23?(s=c,c=A,A=E):s=p+145968|0,E=l[e[c+8>>2]+15|0];i:if(T)E=l[e[ee+96>>2]+(l[e[A+8>>2]+15|0]+z(E,10)|0)|0],l[A+17|0]!=8|(254&l[c+17|0])!=4||(E=8&l[e[s+8>>2]+4|0]?E-15|0:E);else{if(Z=e[ee+100>>2],We=l[e[A+8>>2]+15|0],s=l[A+20|0],E=l[Z+(We+z(s|l[c+20|0]?(0|E)==1:E,10)|0)|0],!s|!(32&l[0|ee]))break i;E=l[1+(Z+z(We,10)|0)|0]+E>>>1|0}s=I>>>1|0,We=!T,I=(0|(E=(0|z(e[130104+(T?(0|T)==1?4:8:0)>>2],E))/128|0))<=8?8:E;i:if((0|Ke)!=7)dt&&(I=e[ee+200>>2]+I|0);else{if(I=(E=e[ee+200>>2])+I|0,!dt)break i;I=((0|E)/2|0)+I|0}E=s&We|(0|f)==27,(s=M[304+(ee+(Ke<<1)|0)>>1])||(s=M[ee+316>>1]),I=z(s<<16>>16,I),(T=l[7+(s=p+145840|0)|0])&&(Ke=l[e[144464+(T<<2)>>2]+14|0])&&(I=(0|z(I,Ke))/100|0),(E|(0|a)==2)==1&&(2097152&(a=e[ee+12>>2])||(I=(0|z(262144&a?282:256+((280-(l[e[8+(p+145840|0)>>2]+14|0]<<1)|0)/3|0)&65535,I))/256|0)),Ke=p+145840|0,a=z(e[32526],e[ee+196>>2]),dt=(0|at)!=2?256:(0|((0|a)>(0|I)?I:a))/128|0,e[Ke+12>>2]=dt,(a=l[Ke+16|0])>>>0>=19&&(Ln(84371,28,MA),g[Ke+16|0]=0,T=l[s+7|0],a=0),I=a+1|0,(a=255&T)?(Hi(a,o+8|0),a=On(e[o+132>>2])):a=e[129280+((255&I)<<2)>>2],E=p+145840|0,1&(m|ce)&&(s=145840+(p=he<<5)|0,m=l[0|a],a=l[E+21|0],a=((0|z(m,l[E+22|0]-a|0))/256|0)+a|0,g[s+22|0]=a,r=(a-(r=(0|a)==255?255:r)|0)>16?a-16|0:r,g[s+21|0]=r,s=0,(0|r)<(0|a)&&(g[Ke+16|0]=I,s=2),e[12+(r=p+145840|0)>>2]=dt,g[r+16|0]=s,a=l[cA+19|0],g[r+19|0]=l[r+17|0]!=3&&a>>>0>18?18:a),r=(0|at)!=2,s=-2&(a=M[A>>1]),b[A>>1]=s;i:{c:{o:switch(l[A+17|0]-3|0){case 5:if(l[c+17|0]==2)break i;s=1|a;break c;case 0:break o;default:break i}if(b[A>>1]=1|a,l[c+17|0]!=2&&e[e[A+8>>2]>>2]!=12146)break i}b[A>>1]=s}r?(0|(A=r<<4))<=((a=l[E+22|0])-(s=l[E+21|0])|0)||(s=(0|(A=a-A|0))>0?A:0,g[E+21|0]=s):(a=l[E+22|0],s=l[E+21|0]),A=255&s,r=((0|z(l[e[129280+(l[Ke+16|0]<<2)>>2]+127|0],a-A|0))/256|0)+A|0,m=0,ce=0;break e}s=e[12+(a+145840|0)>>2],e[c+12>>2]=s,(0|at)==3&&(s=e[32526],e[c+12>>2]=s),a=r;s:switch(E-5|0){case 0:e[c+12>>2]=(z(s,160)>>>0)/100;break r;case 2:break s;default:break A}e[c+12>>2]=(z(s,120)>>>0)/100}a=r}ce=0,g[16+(A=p+145840|0)|0]=0,s=A,a=(A=255&a)-16|0,g[s+21|0]=A>>>0>=a>>>0?a:0}if(!(e[36423]>(0|i)))break}if(J=o+160|0,15&(s=e[47197])|e[36456]){A=0,c=0,J=a=J-80|0;e:if((r=e[33222])||(e[33223]=500,r=Ct(500),e[33222]=r,r)){if(!((e[36423]-2|0)<2)){for(A=s>>8,ee=128&s?0:A,T=A&s<<24>>31,E=2&s,s=a+32|1,p=1;;){if(rs(a,e[8+(i=145840+(he=p<<5)|0)>>2],i,E,a+72|0),A=a+32|0,(13&(r=l[i+20|0]))==1&&(g[a+32|0]=32,A=s),!ee|(0|ee)!=32&!!(0|r)|p>>>0<2||(Gt(a+76|0,a),e[a+76>>2]-880>>>0>4294967103||(A=zr(ee,A)+A|0)),4&l[0|i]&&((r=l[3+(he+145840|0)|0])>>>0<2||(r=r>>>0>=5?5:r,r=E?r>>>0>3?712:716:g[r+94144|0],e[a+76>>2]=r,A=zr(r,A)+A|0)),I=0,e[a+72>>2]=0,l[0|(r=a)])for(;r=Gt(a+76|0,r)+r|0,e[a+72>>2]>>>I-1&1|!T|(0|I)<=0||(m=e[a+76>>2])-880>>>0>4294967103||vi(m)&&(A=zr(T,A)+A|0),I=I+1|0,A=zr(e[a+76>>2],A)+A|0,l[0|r];);if(l[e[i+8>>2]+10|0]!=21&&(8&(r=M[i>>1])&&(A=rs(A,e[36128],i,E,0),r=M[i>>1]),!(4&r)|l[17+(he+145840|0)|0]==2||(A=rs(A,e[36136],i,E,0)),(r=l[7+(he+145840|0)|0])&&(A=rs(A,e[144464+(r<<2)>>2],i,E,0))),(A=(i=A-(a+32|0)|0)+c|0)>>>0<ue[33223])r=e[33222];else{if(r=A+500|0,e[33223]=r,!(r=Ur(e[33222],r))){e[33223]=0,r=86135;break e}e[33222]=r}if(g[(I=i)+(i=a+32|0)|0]=0,qA(r+c|0,i),c=A,!((0|(p=p+1|0))<(e[36423]-2|0)))break}if(!r){r=86135;break e}}g[A+r|0]=0}else e[33223]=0,r=86135;J=a+80|0,15&l[188788]&&(e[t>>2]=r,fr(e[47195],84367,t)),(A=e[36456])&&ct[0|A](r)}l[190280]?(e[36423]=0,A=1):(oA(0),(A=e[t+8>>2])?(J=r=J+-64|0,ar(r,A,60),Ni(r,1),a=0,(A=Tt(r,0))&&(a=A,l[202976]&&(a=Tt(202976,2))),J=r- -64|0,e[44468]=a):a=e[44468],A=1,a&&(r=e[32972],(a=Ct(1344))&&(r=wt(a,r,1344),a=216192+(e[50758]<<4)|0,e[a>>2]=11,e[a+8>>2]=r,r=e[50758]+1|0,e[50758]=(0|r)<=169?r:0),e[44468]=0))}else A=0,g[190280]=0;else A=0,e[36423]=0,e[50758]=0,e[50757]=0;return J=t+16|0,A}function Se(A,t){var r=0,a=0,s=0,i=0;r=1073741825;e:{A:{r:{s:{i:{c:{o:{u:{l:{n:{p:{C:{m:{b:{h:{E:{w:{y:{L:{W:{V:{M:{B:{D:{F:{q:{Y:{Z:{S:{se:{H:{ye:{U:{d:{f:{$:{k:{ee:{I:{a:{We:{J:{t:{fe:{R:{pe:{he:{Ve:{le:{N:{Ee:{v:{Me:{_:{me:{Ge:{je:{De:{Qe:{Te:{Pe:{ze:{Fe:{Ce:{Ue:{Ae:{Se:{Ke:{Ne:{Oe:{G:{be:{Ze:{hA:{mA:{CA:{bA:{IA:{wA:{kA:{xA:{BA:{vA:{yA:{EA:{MA:{GA:{DA:{QA:{TA:{PA:{zA:{FA:{SA:{NA:{OA:{LA:{YA:{HA:{RA:{WA:{VA:{jA:switch(0|t){case 0:t=A-9>>>0<5?1073741825:0,t=(A=(0|A)==133)?1073741825:t;break F;case 1:g:{Q:{T:{P:{z:{O:{X:{j:{te:{ie:{re:{ne:{ce:{ge:{if((0|(t=-256&A))<=2047){if(!t)break ge;if((0|t)==1536)break ce;if((0|t)!=1792||(r=0,a=1,(0|A)!=1807))break g;break t}if((0|t)<=69631){if((0|t)==2048)break ne;if((0|t)!=8192)break g;switch(r=131076,A-8204|0){case 1:break re;case 0:break t;default:break ie}}if((0|t)==69632)break te;if((0|t)!=917504)break g;switch(r=8388608,A-917505|0){case 62:break T;case 58:break P;case 57:break z;case 45:break O;case 43:break X;case 32:break j;case 0:break t;default:break Q}}if(r=16,(0|A)!=173)break g;break t}if(r=0,a=1,A-1536>>>0<6)break t;a=(0|A)==1757,t=(A=(0|A)==1564)?2:0;break D}if(r=0,a=1,(0|A)!=2274)break g;break t}return $=64,4}if(r=1073741826,(-2&A)==8206)break t;if(A-8234>>>0<5)return $=0,2;if(r=128,A-8289>>>0<4||(r=2,A-8294>>>0<4))break t;if(r=8388608,A-8298>>>0>=6)break g;break t}t=!(A-69821&-17),A=0;break B}$=536870976;break M}$=268435520;break M}$=-2147483584;break M}$=134217792;break M}$=67108928;break M}$=1073741888;break M}if(r=131072,a=64,A-917536>>>0<96)break t}break a;case 2:g:{Q:{T:{P:{z:{O:{X:{if((0|(t=-256&A))<=130303){if((0|t)<=127743){if((0|t)<=64767){if((0|t)<=11007){if((0|t)==8192)break X;if((0|t)!=9216)break g;if(A-9255>>>0>=25)break O;break I}if((0|t)==11008)break z;if((0|t)!=11776||(r=-2147483648,A-11845>>>0>=59))break g;break t}if((0|t)<=126975){if((0|t)==64768)break P;if((0|t)!=65280||(r=4194304,A-65520>>>0>=9))break g;break t}if((0|t)==126976|(0|t)==127232|(0|t)==127488)break J;break 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t}if(r=4194304,(0|A)!=8293)break g;break t}if(r=-2147483648,A-9291>>>0>=21)break g;break t}if((-16&A)==11248|A-11219>>>0<25|(0|A)==11209|A-11194>>>0<3||(0|(t=-2&A))==11124)break I;if(r=-2147483648,(0|t)!=11158)break g;break t}if(r=65536,A-64976>>>0>=32)break g;break t}if((0|A)==917504)break ee;if(r=4194304,A-917506>>>0>=30)break g;break t}if(r=4194304,A>>>0>917999)break t}t=(A=!(65534&~A))>>>16|0,A<<=16;break B;case 6:g:{Q:{T:{P:{z:{O:{X:{j:{te:{ie:{re:{ne:{if((0|(t=-256&A))<=7679){if((0|t)<=767){if(!t)break ne;if((0|t)==256)break re;if((0|t)!=512)break a;if((0|A)!=585)break ie;break V}if((0|t)==768)break te;if((0|t)==1024)break j;if((0|t)!=7424||(r=16777216,(0|A)!=7574))break a;break t}if((0|t)<=119807){if((0|t)==7680)break X;if((0|t)==8448)break O;if((0|t)!=65280||(r=256,A-65345>>>0>=6))break a;break t}if((0|t)<=120319){if((0|t)==119808)break z;if((0|t)!=120064)break a;if(A>>>0>=120070)break P;break k}if((0|t)==120320)break T;if((0|t)!=120576)break a;if(A>>>0>=120597)break Q;break k}if(r=768,A-97>>>0<6)break t;if(r=16777216,A-105>>>0>=2)break a;break t}r=(t=(0|A)==329)>>>9|0,t=(A=(0|A)==303)?16777216:t<<23;break W}if((0|A)==616)break V;if(r=16777216,(0|A)!=669)break a;break t}switch(r=128,A-976|0){case 35:break g;case 0:case 1:case 2:case 5:case 32:case 33:break t}if((-2&A)!=1012)break a;break t}a=(A=!(A-1110&-3))>>>8|0,A<<=24;break L}a=(t=(0|A)==7883)>>>8|0,t=(A=(0|A)==7725)?16777216:t<<24;break D}if(A-8458>>>0<10)break k;if((t=A-8495|0)>>>0<11)break VA;break fe}if((0|(t=-2&A))==119842)break $;if(A-119808>>>0<85)break k;if(A-119894>>>0<2|A-119946>>>0<2)break $;if((0|A)==119995|A-119896>>>0<69|A-119982>>>0<12)break k;if((0|t)==119998)break $;if(A-119997>>>0<7)break k;if(r=16777344,(0|t)==120050)break t;if(r=128,A>>>0<=120004)break a;break t}if((-2&A)==120102)break $;if(A-120094>>>0<28)break k;P:{if((0|A)<=120257){if(A-120154>>>0<2)break $;if(r=16777344,A-120206>>>0>=2)break P;break t}if(A-120258>>>0<2)break $;if(r=16777344,A-120310>>>0<2)break t}if(r=128,A>>>0<=120145)break a;break t}if(A-120362>>>0<2|A-120414>>>0<2)break $;if(r=16777344,A-120466>>>0<2)break t;if(A-120540>>>0<31|A>>>0>120571|A>>>0<120486)break k;if(r=128,A-120514>>>0>=25)break a;break t}if(A-120772>>>0<8|A-120746>>>0<25|A-120714>>>0<31|A-120688>>>0<25||(0|A)!=120597&A>>>0<120629|A-120656>>>0<31)break k;if(r=128,A-120630>>>0<25)break t;break a}break V;case 7:g:{Q:{T:{P:{z:{O:{X:{j:{te:{ie:{re:{ne:{ce:{ge:{x:{oe:{ae:{K:{ue:{de:{Ie:{we:{if((0|(t=-256&A))<=11263){if((0|t)<=3583){if((0|t)<=1535){if((0|t)==512)break we;if((0|t)==768)break Ie;if((0|t)!=1280||(r=4096,(0|A)!=1369))break a;break t}if((0|t)==1536)break de;if((0|t)==1792)break ue;if((0|t)!=2304||(r=4096,(0|A)!=2417))break a;break t}if((0|t)<=7167){if((0|t)==3584)break K;if((0|t)==6144)break ae;if((0|t)!=6656||(r=8192,(0|A)!=6823))break a;break t}if((0|t)==7168)break oe;if((0|t)==7424)break x;if((0|t)!=8192)break a;if(r=16793600,!(t=A-8305|0))break t;if((0|t)==14)break ge;break ce}if((0|t)<=43263){if((0|t)<=40959){if((0|t)==11264)break ne;if((0|t)==11776)break re;if((0|t)!=12288)break a;switch(r=8192,A-12293|0){case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:case 16:case 17:case 18:case 19:case 20:case 21:case 22:case 23:case 24:case 25:case 26:case 27:case 28:case 29:case 30:case 31:case 32:case 33:case 34:case 35:case 36:case 37:case 38:case 39:case 40:case 41:case 42:case 43:break te;case 0:case 44:case 45:case 46:case 47:case 48:break t;default:break ie}}if((0|t)==40960)break j;if((0|t)==42496)break X;if((0|t)!=42752)break a;if(A-42775>>>0>=9)break O;break f}if((0|t)<=65279){if((0|t)==43264)break z;if((0|t)==43520)break P;if((0|t)!=43776||(r=20480,(-4&A)!=43868))break a;break t}if((0|t)==65280)break T;if((0|t)==92928)break Q;if((0|t)!=93952)break a;if(r=4096,A-94099>>>0<13)break t;if(r=8192,(-2&A)!=94176)break a;break t}if(r=16797696,(0|A)==690)break t;if(A-688>>>0<9)return $=0,20480;if(A-697>>>0<7)break f;if((0|(t=-2&A))==704)return $=0,20480;if(r=4096,A-710>>>0<10||(r=12288,(0|t)==720)||(r=20480,A-736>>>0<5))break t;r=(A=(-3&A)==748)>>>20|0,A<<=12;break y}t=(0|A)==890?20480:0,t=(A=(0|A)==884)?4096:t;break F}if(r=8192,(0|A)==1600)break t;if(r=4096,A-1765>>>0>=2)break a;break t}if(r=4096,(-2&A)==2036)break t;if(r=8192,(0|A)!=2042)break a;break t}t=(A=!(A-3654&-129))>>>19|0,A<<=13;break B}if(r=8192,(0|A)!=6211)break a;break t}if(r=12288,(0|A)==7291)break t;if(r=4096,A-7288>>>0>=6)break a;break t}if(r=16797696,(0|A)==7522||(r=20480,A-7468>>>0<63))break t;switch(r=16384,A-7588|0){default:if((0|A)==7544)break t;case 1:case 2:case 3:if(A-7579>>>0>=37)break a;break t;case 0:case 4:}return $=0,16793600}return $=0,16384}if(r=16384,A-8336>>>0>=13)break a;break t}t=(r=(0|A)==11389)>>>18|0,r=(A=(0|A)==11388)?16793600:r<<14;break w}if(r=-2147479552,(0|A)!=11823)break a;break t}if(A-12445>>>0<2)break t;if((0|A)==12540)break g}if(A-12541>>>0>=2)break a;break t}if(r=8192,(0|A)!=40981)break a;break t}if((0|A)==42508)break E;if((0|A)==42623)break f;if(r=20480,(-2&A)!=42652)break a;break t}if((0|A)==42864)return $=0,16384;if((0|A)==42888)break f;if(r=20480,(-2&A)!=43e3)break a;break t}r=(t=(0|A)==43494)>>>19|0,t=(A=(0|A)==43471)?8192:t<<13;break W}if((0|A)==43632||(0|A)==43741)break E;if(r=8192,A-43763>>>0>=2)break a;break t}if(r=12288,(0|A)==65392)break t;if(r=135168,(-2&A)!=65438)break a;break t}if(r=8192,(-2&A)==92994)break t;break a}return $=0,12288;case 8:r=128;g:{Q:{T:{P:{z:{O:{X:{j:{te:{if((0|(t=-256&A))<=12543){if((0|t)<=5887){if((0|t)<=3583){if(!t)break te;if((0|t)!=1536||(t=8388608,(0|A)!=1651))break v;break le}if((0|t)==3584)break j;if((0|t)!=4352||(r=4194304,A-4447>>>0>=2))break v;break N}if((0|t)<=8447){if((0|t)==5888)break X;if((0|t)!=6400||(t=A-6581|0)>>>0>=6)break v;r=e[(t=81432+(t<<3)|0)>>2],a=e[t+4>>2];break N}if((0|t)==8448)break O;if((0|t)!=12288||(t=2048,(0|A)!=12294))break v;break le}if((0|t)<=68863){if((0|t)<=63999){if((0|t)==12544)break z;if((0|t)!=43520)break v;if((t=A-43701|0)>>>0<8)break Q;break Ee}if((0|t)==64e3)break g;if((0|t)!=65280||(t=4194304,(0|A)!=65440))break v;break le}if((0|t)<=100095){if((0|t)==68864)break P;if((0|t)!=70400||(r=8192,(0|A)!=70493))break v;break he}if((0|t)==100096)break T;if((0|t)!=126464)break v;break N}a=(t=!(A-170&-17))>>>18|0,r=t<<14;break N}if(r=33554432,A-3648>>>0<5)break N;if(t=0,i=33554432,(0|A)==3759)break le;if(A-3776>>>0>=5)break v;break N}if(r=8388608,A-6051>>>0>=2)break v;break N}if(A-8501>>>0>=4)break v;break N}if(t=4194304,(0|A)!=12644)break v;break le}if(r=4096,(-2&A)!=68898)break v;break N}if(r=2048,A-100333>>>0<5)break N;break v}if(r=33554432,!(211>>>t&1))break Ee;break N}if((t=A-64014|0)>>>0>=28)break v;r=e[(t=81480+(t<<3)|0)>>2],a=e[t+4>>2];break N;case 10:g:{Q:{T:{P:{z:{O:{if((0|(t=-256&A))<=119807){if((0|t)<=8447){if(!t)break O;if((0|t)!=768)break a;switch(r=128,A-976|0){case 0:case 1:case 2:case 36:case 37:break t;default:break a}}if((0|t)==8448)break z;if((0|t)!=65280||(r=256,A-65313>>>0>=6))break a;break t}if((0|t)<=120319){if((0|t)==119808)break P;if((0|t)!=120064)break a;if(A>>>0>=120070)break T;break k}if((0|t)==120320)break Q;if((0|t)!=120576)break a;if(A-120772>>>0>=8)break g;break k}if(r=768,A-65>>>0>=6)break a;break t}switch(r=128,A-8450|0){case 0:case 5:break t}if(A-8458>>>0<10)break k;if((t=A-8469|0)>>>0<20)break WA;if((-4&A)==8508)break t;break Me}if(A-119982>>>0<12|A>>>0>120004|A-119977>>>0<4|A-119973>>>0<2||(0|A)==119970|(-2&A)==119966|A-119808>>>0<85)break k;if(r=128,A-119894>>>0>=71)break a;break t}if(A-120138>>>0<7|A>>>0>120145|(0|A)==120134|A-120128>>>0<5||A-120123>>>0<4|A-120094>>>0<28|(0|A)!=120070&A>>>0<120075|A-120086>>>0<7)break k;if(r=128,A-120077>>>0>=8)break a;break t}if(A-120540>>>0<31|A>>>0<120486)break k;if(r=128,A-120488>>>0>=25)break a;break t}if(A-120714>>>0<31|A-120598>>>0<31)break k;if(r=128,A-120656>>>0<31)break t;break a;case 11:g:{Q:{T:{P:{z:{O:{X:{j:{te:{ie:{re:{ne:{ce:{ge:{x:{oe:{ae:{if((0|(t=-256&A))<=43263){if((0|t)<=3839){if((0|t)<=3071){if((0|t)==2304)break ae;if((0|t)!=2816)break d;if((0|A)>3005)break x;if((0|A)!=2878)break oe;return $=0,132096}if((0|t)==3072)break ge;if((0|t)!=3328)break d;switch(r=132096,A-3535|0){case 0:case 16:break t;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:break d;default:break ce}}if((0|t)<=6911){if((0|t)==3840)break ne;if((0|t)!=4096)break d;if(r=1024,A-4139>>>0<2)break t;switch(A-4145|0){case 0:case 7:case 10:case 11:case 37:case 38:case 49:case 54:case 55:case 82:case 83:break t;case 86:case 87:case 88:case 89:case 90:case 91:case 94:case 105:case 106:break g;default:break re}}if((0|t)==6912)break ie;if((0|t)==7168)break te;if((0|t)!=12288||(r=135168,(-2&A)!=12334))break d;break t}if((0|t)<=70399){if((0|t)<=43775){if((0|t)==43264)break j;if((0|t)!=43520)break d;return $=0,A-43643&-3?1024:4096}if((0|t)==43776)break X;if((0|t)==69888)break O;if((0|t)!=70144||(r=4096,(0|A)!=70197))break d;break t}if((0|t)<=70911){if((0|t)==70400)break z;if((0|t)!=70656)break d;t=(0|A)==70845?132096:1024,t=(A=(0|A)==70832)?132096:t;break F}if((0|t)==70912)break P;if((0|t)==71168)break T;if((0|t)!=119040)break d;switch(r=131072,A-119141|0){case 8:break f;case 1:break a;case 0:break t;default:break Q}}t=(0|A)==2519?132096:1024,t=(A=(0|A)==2494)?132096:t;break F}if(r=132096,(0|A)!=2903)break d;break t}if((0|A)==3006)return $=0,132096;if(r=132096,(0|A)!=3031)break d;break t}if((A=A-3266|0)>>>0>20||(r=132096,!(1<<A&1572865)))break d;break t}if((0|A)==3390)break t;if((0|A)!=3415)break d;break t}if(r=4096,(-2&A)!=3902)break d;break t}r=(A=(0|A)==4252)>>>22|0,A<<=10;break y}switch(r=1024,A-6965|0){default:if((0|A)==6916)break t;break;case 0:case 6:break t;case 1:case 2:case 3:case 4:case 5:}if(A-6973>>>0<5)break t;switch(A-6979|0){case 1:break f;case 0:break t}if((0|A)==7042|(0|A)==7073|(-2&A)==7078)break t;if((0|A)==7082)break f;if((0|A)==7143|A-7146>>>0<3)break t;r=(A=(0|A)==7150)>>>22|0,A<<=10;break y}t=(0|A)==7415?4096:1024,t=(A=(0|A)==7393)?4096:t;break F}t=(0|A)==43456?4096:1024,t=(A=(0|A)==43347)?4096:t;break F}if(r=4096,(0|A)!=44012)break d;break t}if(r=4096,(0|A)!=70080)break d;break t}r=132096;z:switch(A-70462|0){case 0:case 25:break t;case 15:break z;default:break d}break f}if(r=132096,(0|A)!=71087)break d;break t}if(r=4096,(0|A)!=71350)break d;break t}if(r=135168,A-119150>>>0<5)break t;break d}break f;case 12:t=((0|A)==8419)<<6,A=0;break B;case 13:g:{Q:{T:{P:{z:{O:{X:{j:{te:{ie:{re:{ne:{ce:{ge:{x:{oe:{ae:{K:{ue:{de:{Ie:{we:{Le:{ke:{Ye:{Xe:{qe:{Je:{_e:{$e:{eA:{AA:{tA:{rA:{aA:{xe:{sA:{iA:{nA:{oA:{lA:{He:{Be:{Re:{cA:{gA:{uA:{ve:{dA:{fA:{pA:{if((0|(t=-256&A))<=43775){if((0|t)<=5887){if((0|t)<=2559){if((0|t)<=1535){if((0|t)==768)break pA;if((0|t)==1024)break fA;if((0|t)!=1280)break a;if(A-1425>>>0>=17)break dA;break 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AA;if((0|t)!=7424)break a;if(r=4096,A-7620>>>0<12||(r=1024,A-7655>>>0<14))break t;if((A=A-7669|0)>>>0>=11)break a;t=e[(A=82104+(A<<3)|0)>>2];break h}if((0|t)<=42495){if((0|t)<=11519){if((0|t)==8192)break eA;if((0|t)!=11264||(r=4096,A-11503>>>0>=3))break a;break t}if((0|t)==11520)break $e;if((0|t)!=12288)break a;if(A-12330>>>0>=4)break _e;break f}if((0|t)<=43263){if((0|t)==42496)break Je;if((0|t)!=43008)break a;if(A-43045>>>0>=2)break qe;break d}if((0|t)==43264)break Xe;if((0|t)!=43520)break a;switch(r=1024,A-43561|0){case 83:case 150:case 152:break f;case 0:case 1:case 2:case 3:case 4:case 5:case 8:case 9:case 12:case 13:case 26:case 35:case 135:case 137:case 138:case 139:case 142:case 143:case 149:break t;default:break Ye}}if((0|t)<=71423){if((0|t)<=69375){if((0|t)<=66047){if((0|t)==43776)break ke;if((0|t)==64256)break Le;if((0|t)!=65024)break a;if(r=536870912,A-65024>>>0<15||(a=64,(0|A)==65039))break t;if(r=4096,a=0,(-16&A)!=65056)break a;break t}if((0|t)<=68095){if((0|t)==66048)break we;if((0|t)!=66304||(r=1024,A-66422>>>0>=5))break a;break t}if((0|t)==68096)break Ie;if((0|t)!=68864||(r=5120,(-4&A)!=68900))break a;break t}if((0|t)<=70399){if((0|t)<=69887){if((0|t)==69376)break de;if((0|t)!=69632)break a;if(A-69688>>>0>=14)break ue;break d}if((0|t)==69888)break K;if((0|t)!=70144)break a;if(r=1024,A-70191>>>0<3)break t;switch(A-70196|0){case 2:break f;case 0:case 3:case 10:break t;case 1:case 4:case 5:case 6:case 7:case 8:case 9:break oe;default:break ae}}if((0|t)<=70911){if((0|t)==70400)break x;if((0|t)!=70656)break a;if((-8&A)!=70712)break ge;break d}if((0|t)==70912)break ce;if((0|t)!=71168)break a;if(r=1024,A-71219>>>0<8)break t;switch(A-71229|0){case 2:break f;case 0:case 3:break t;case 1:break re;default:break ne}}if((0|t)<=92927){if((0|t)<=72703){if((0|t)==71424)break NA;if((0|t)==71680)break ie;if((0|t)!=72192)break a;if(A-72193>>>0>=10)break te;break d}if((0|t)<=73215){if((0|t)==72704)break j;if((0|t)!=72960)break a;if((t=A-73009|0)>>>0<19)break FA;break _}if((0|t)==73216)break X;if((0|t)!=92672||(r=4096,A-92912>>>0>=5))break a;break t}if((0|t)<=122879){if((0|t)<=113663){if((0|t)==92928)break O;if((0|t)!=93952||(r=4096,A-94095>>>0>=4))break a;break t}if((0|t)==113664)break z;if((0|t)!=119040)break a;switch(r=4096,A-119143|0){case 0:case 1:case 2:case 20:case 21:case 22:case 23:case 24:case 25:case 26:case 27:case 30:case 31:case 32:case 33:case 34:case 35:case 36:case 67:case 68:case 69:case 70:break t;default:break a}}if((0|t)<=125183){if((0|t)==122880)break P;if((0|t)!=124928||(r=4096,A-125136>>>0>=7))break a;break t}if((0|t)==125184)break T;if((0|t)!=917760||(r=536870912,A-917760>>>0>=240))break a;break t}if(A-768>>>0<69)break f;if(r=21504,(0|A)==837)break t;if(A-838>>>0<9)break f;if(r=4194304,(0|A)==847)break t;if((-8&A)==848)break f;if(r=4096,A-861>>>0>=6)break a;break t}if(r=4096,A-1155>>>0>=5)break a;break t}if(r=4096,A-1443>>>0<13||(r=5120,A-1456>>>0<14))break t;if((A=A-1471|0)>>>0>=9)break a;t=e[(A=81944+(A<<3)|0)>>2];break h}if(A-1552>>>0<11)break d;if(r=5120,A-1611>>>0<8)break t;if(A-1619>>>0<4)break d;switch(A-1623|0){case 1:break f;case 0:break t;case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 25:case 127:case 128:case 129:case 130:case 131:case 132:case 133:break d}if(r=4096,A-1759>>>0<2)break t;if((t=A-1761|0)>>>0<8)break RA;break me}if(A-1856>>>0<11)break f;if(r=5120,A-1958>>>0<11)break t;if(r=4096,A-2027>>>0>=9)break a;break t}switch(r=1024,(-2&A)-2070|0){case 2:break f;case 0:break t}if(A-2260>>>0<12|A-2089>>>0<4|A-2075>>>0<9|A-2085>>>0<3)break d;if(A-2275>>>0<7)return $=0,5120;if(r=4096,A-2282>>>0<6||(r=5120,A-2288>>>0<15))break t;if(r=1024,(0|A)!=2303)break a;break t}switch(r=1024,A-2362|0){case 2:break f;case 0:break t}if(A-2369>>>0<8)break d;switch(r=4096,A-2381|0){case 0:case 4:case 5:case 6:case 7:case 111:break t;case 8:case 9:case 10:case 21:case 22:case 52:break d}if(A-2497>>>0<4)break d;if((0|A)==2509)break t;if(r=1024,(-2&A)!=2530)break a;break t}if(A-2561>>>0<2)break d;switch(r=4096,A-2620|0){case 0:case 17:case 128:case 145:break t;case 5:case 6:case 11:case 12:case 15:case 16:case 21:case 52:case 53:case 57:case 69:case 70:case 133:case 134:case 135:case 136:case 137:case 139:case 140:case 166:case 167:case 190:case 191:case 192:break d}if(A-2813>>>0>=3)break a;break t}if((0|A)==2817)break d}if(A-2881>>>0<4)break d;switch(r=4096,A-2893|0){case 0:break t;case 9:break d}if((-2&A)==2914)break d;switch(r=1024,A-3008|0){default:if((0|A)!=2946)break a;break t;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:break a;case 0:break t;case 13:}break f}switch(r=1024,A-3072|0){case 77:case 188:case 205:break f;case 0:case 62:case 63:case 64:case 70:case 71:case 72:case 74:case 75:case 76:case 85:case 86:case 98:case 99:case 129:case 191:case 198:case 204:break t}if((-2&A)!=3298)break a;break t}if(r=4096,A-3387>>>0<2)break t;if(A-3393>>>0<4)break d;if((0|A)==3405)break t;if((0|t)==3426)break d;switch(A-3530|0){case 0:break t;case 8:case 9:case 10:case 12:break d;default:break a}}if((t=A-3633|0)>>>0<10)break HA;break Ge}if((r=A-3893|0)>>>0>4|!(1<<r&21))break je;break f}switch(r=1024,A-4141|0){case 10:case 12:case 13:break f;case 0:case 1:case 2:case 3:case 5:case 6:case 7:case 8:case 9:case 16:case 17:case 43:case 44:case 49:case 50:case 51:case 68:case 69:case 70:case 71:case 85:case 88:case 89:break t}t=(r=(0|A)==4253)>>>22|0,r=(A=(0|A)==4237)?4096:r<<10;break w}r=1024;xe:{switch(A-5906|0){case 0:case 1:case 32:case 33:break t;case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:case 16:case 17:case 18:case 19:case 20:case 21:case 22:case 23:case 24:case 25:case 26:case 27:case 28:case 29:case 30:case 31:break xe}switch(A-5970|0){case 0:case 1:case 32:case 33:break t;default:break xe}}if(r=4194304,(-2&A)==6068)break t;if((t=A-6071|0)>>>0<16)break YA;break De}if(r=536870912,A-6155>>>0<3||(r=67109888,A-6277>>>0<2))break t;if(r=1024,(0|A)!=6313)break a;break t}if(A-6679>>>0<2)break d;switch(r=1024,A-6683|0){case 0:case 59:case 61:case 62:case 63:case 64:case 65:case 66:case 67:case 71:case 74:case 75:case 76:case 77:case 78:case 79:case 80:case 81:case 88:case 89:break t}if(r=4096,A-6832>>>0<14)break t;if((A=A-6773|0)>>>0>=11)break a;t=e[(A=82016+(A<<3)|0)>>2];break h}if(A-6966>>>0<5)break t;switch(A-6972|0){case 0:case 6:break t}if(A-7019>>>0<9)break f;switch(A-7040|0){case 43:break f;case 0:case 1:case 34:case 35:case 36:case 37:case 40:case 41:case 44:case 45:case 104:case 105:case 109:case 111:case 112:case 113:break t;default:break a}}if(r=1024,A-7212>>>0<8)break t;switch(r=12288,A-7222|0){case 1:break f;case 0:break t}if(A-7380>>>0<13)break f;switch(r=4096,A-7376|0){case 0:case 1:case 2:case 18:case 19:case 20:case 21:case 22:case 23:case 24:case 29:case 36:break t}if((-2&A)!=7416)break a;break t}if(r=128,A-8400>>>0<13)break t;if((A=A-8417|0)>>>0>=15)break a;t=e[(A=82192+(A<<3)|0)>>2];break h}if(r=1024,(-32&A)!=11744)break a;break t}if(r=4096,A-12441>>>0>=2)break a;break t}if((0|A)==42607)break f;if(A-42612>>>0<8)break d;if((0|(A&=-2))==42620)break f;if((0|A)==42654)break g;if(r=4096,(0|A)!=42736)break a;break t}switch(r=4096,A-43204|0){case 0:break t;case 1:break d}if(A-43232>>>0>=18)break a;break t}if(A-43302>>>0<5)break d;if(A-43307>>>0<3)break f;if(A-43335>>>0<11|A-43392>>>0<3)break d;if(r=4096,(0|A)==43443||(r=1024,A-43446>>>0<4))break t;a=(t=(0|A)==43493)>>>20|0,t=(A=(0|A)==43452)?1024:t<<12;break D}if((-2&A)==43756)break t;if(r=4096,(0|A)!=43766)break a;break t}r=1024;ke:switch(A-44005|0){case 0:case 3:break t;case 8:break ke;default:break a}break f}if(r=5120,(0|A)!=64286)break a;break t}if(r=4096,(0|A)!=66272)break a;break t}if((-4&A)==68108)break d;if((t=A-68097|0)>>>0<6)break LA;break Qe}if(r=4096,A-69446>>>0>=11)break a;break t}if(A-69811>>>0<4)break d;if(r=1024,(0|A)==69633)break t;if(r=4096,A-69817>>>0>=2)break a;break t}switch(r=1024,A-69888|0){case 51:case 52:case 115:break f;case 0:case 1:case 2:case 39:case 40:case 41:case 42:case 43:case 45:case 46:case 47:case 48:case 49:case 50:break t}if((-2&A)==70016|A-70070>>>0<9)break t;if(r=4096,A-70090>>>0>=3)break a;break t}if((0|A)==70367)break t}if(A-70371>>>0<6)break t;if(r=4096,A-70377>>>0>=2)break a;break t}if((-2&A)==70400)break d;r=4096;x:switch(A-70460|0){case 0:case 42:case 43:case 44:case 45:case 46:case 47:case 48:case 52:case 53:case 54:case 55:case 56:break t;case 4:break x;default:break a}break d}if((0|A)==70722)break f;if(A-70723>>>0<2)break d;if((t=A-70835|0)>>>0<=13)break Q;break Te}if((t=A-71090|0)>>>0<12)break OA;break Pe}switch(A-71339|0){case 0:case 2:break t;default:break re}}if(A-71344>>>0<6)break t;if(r=4096,(0|A)!=71351)break a;break t}if(r=1024,A-71727>>>0<10)break t;if(r=4096,A-71737>>>0>=2)break a;break t}if((0|A)==72244)break f;if(A-72245>>>0<10)break d;if(r=4096,(0|A)==72263)break t;if(A-72273>>>0<11)break d;if(r=1024,A-72330>>>0<13)break t;r=(t=(0|A)==72345)>>>20|0,t=(A=(0|A)==72344)?8192:t<<12;break W}if((t=A-72752|0)>>>0<16)break SA;break ze}if(r=1024,A-73459>>>0>=2)break a;break t}if(r=1024,A-92976>>>0>=7)break a;break t}if(r=1024,(0|A)!=113822)break a;break t}if(r=1024,A-122888>>>0<17)break t;if((A=A-122880|0)>>>0>=43)break a;t=e[(A=82816+(A<<3)|0)>>2];break h}if(r=12288,A-125252>>>0<3||(r=1024,(0|A)==125255)||(r=4096,A-125256>>>0<3))break t;break a}if(!(1<<t&12479))break Te;break d}break d;case 14:g:{if((0|(t=-256&A))!=120576){if((0|t)!=65280){if(t||(r=768,a=66,A-48>>>0>=10))break g;break t}if(r=256,A-65296>>>0>=10)break g;break t}if(r=128,A-120782>>>0<50)break t}break a;case 15:g:{if((0|(t=-256&A))!=12288){if((0|t)!=8448)break g;t=(A=(0|(r=-16&A))==8560)>>>18|0,a=A<<14,r=(A=(0|r)==8544)?32768:a;break w}if(A-12321>>>0<9||A-12344>>>0<3)return $=0,2048;if(r=2048,(0|A)==12295)break t}break a;case 16:g:{Q:{T:{if((0|(t=-256&A))<=9215){if((0|t)==4864)break T;if((0|t)!=6400||(r=134217728,(0|A)!=6618))break g;break t}if((0|t)==9216)break Q;if((0|t)!=127232)break g;if(r=0,a=-2147483648,(0|A)==127232)break t;if(a=268435456,A-127233>>>0>=10)break g;break t}if(r=134217728,A-4969>>>0>=9)break g;break t}if(r=0,a=-2147483648,A-9352>>>0<20)break t}break a;case 17:a=(A=(0|A)==8256)>>>25|0,A<<=7;break L;case 18:g:{Q:{T:{P:{if((0|(t=-256&A))<=11775){if((0|t)<=6143){if(r=-2147483624,!t)break t;if((0|t)!=1280||(r=24,(0|A)!=1418))break g;break t}if((0|t)==6144)break P;if((0|t)!=8192)break g;if(r=-2147483624,(-2&A)==8208)break t;A=A-8211>>>0<2,t=-2147483640;break b}if((0|t)<=65023){if((0|t)==11776)break T;if((0|t)!=12288)break g;t=(0|A)==12336,r=(A=(0|A)==12316)||t?-2147483640:8,$=A?0:t?130:0;break m}if((0|t)==65024)break Q;if((0|t)!=65280||(r=24,(0|A)!=65293))break g;break t}if(r=24,(0|A)!=6150)break g;break t}if(r=-2147483624,(0|A)==11799)break t;A=(-2&A)==11834,t=-2147483640;break b}if(r=8,a=8388608,A-65073>>>0<2||(r=152,a=0,(0|A)==65123))break t}return $=0,8;case 19:g:{Q:{T:{P:{z:{O:{if((0|(t=-256&A))<=11775){if((0|t)<=8959){if(t)break O;break I}if((0|t)==8960)break z;if((0|t)==9984)break P;if((0|t)!=10496)break a;break U}if((0|t)<=64767){if(r=-2147483648,(0|t)==11776)break t;if((0|t)!=12288)break a;switch(r=-2147483616,A-12301|0){case 0:case 2:break t;default:break T}}if((0|t)==64768)break Q;if((0|t)==65024)break g;if((0|t)!=65280)break a;if(r=32,(0|A)==65379)break t;break a}if((0|t)!=8192)break a;switch(A-8318|0){default:if(r=-2147483648,(0|A)!=8262)break a;break t;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:break a;case 0:case 16:}break k}switch(r=-2147483520,A-8969|0){case 1:break a;case 0:case 2:break t}if((0|A)!=9002)break a;return $=0,-2139095040}if(r=-2147483520,(0|A)==10182)break t;break ye}return $=0,(-2&A)==12318?-2147483616:-2147483648}if(r=-2147483648,(0|A)!=64830)break a;break t}a=(A=!(A-65090&-3))>>>27|0,A<<=5;break L;case 20:case 21:if(!(A&=-256))break C;if(r=-2147483616,(0|A)==8192)break t;break We;case 22:g:{Q:{T:{P:{z:{O:{X:{j:{te:{ie:{re:{ne:{ce:{ge:{x:{oe:{ae:{K:{ue:{de:{Ie:{we:{Le:{ke:{Ye:{Xe:{qe:{Je:{_e:{$e:{eA:{AA:{tA:{rA:{aA:{xe:{sA:{iA:{nA:{oA:{lA:{He:{Be:{Re:{cA:{gA:{uA:{ve:{if((0|(t=-256&A))<=43519){if((0|t)<=5887){if((0|t)<=2303){if((0|t)<=1535){if(!t)break ve;if((0|t)==768)break uA;if((0|t)!=1280)break a;if(r=0,a=538968064,A-1371>>>0<2)break t;switch(a=268435456,A-1373|0){case 0:break t;case 1:break cA;default:break gA}}if((0|t)==1536)break Be;if((0|t)==1792)break He;if((0|t)!=2048)break a;if((t=A-2103|0)>>>0<8)break PA;break Fe}if((0|t)<=3839){if((0|t)==2304)break lA;if((0|t)==3328)break oA;if((0|t)!=3584||(r=64,(-2&A)!=3674))break a;break t}if((0|t)<=4863){if((0|t)==3840)break nA;if((0|t)!=4096)break a;if(r=268435520,(-2&A)==4170)break t;if(r=0,a=16777216,(0|A)!=4347)break a;break t}if((0|t)==4864)break iA;if((0|t)!=5632)break a;switch(r=64,A-5741|0){case 1:break i;case 0:break t;default:break sA}}if((0|t)<=11263){if((0|t)<=6655){if((0|t)==5888)break xe;if((0|t)==6144)break QA;if((0|t)!=6400)break a;t=(0|A)==6469,r=(A=(0|A)==6468)||t?268435520:0,$=A?536870912:t?1073741824:0;break m}if((0|t)<=7167){if((0|t)==6656)break aA;if((0|t)!=6912)break a;if((0|(t=-2&A))!=7002)break rA;break H}if((0|t)==7168)break tA;if((0|t)!=8192)break a;switch(r=-2147483520,A-8214|0){case 1:break I;case 0:break t;case 16:break AA;default:break eA}}if((0|t)<=41983){if((0|t)==11264)break $e;if((0|t)==11776)break _e;if((0|t)!=12288)break a;switch(r=-2147483584,a=272629760,A-12289|0){case 2:break I;case 0:break t;case 1:break qe;default:break Je}}if((0|t)<=43007){if((0|t)==41984)break Xe;if((0|t)!=42496)break a;switch(r=64,a=268435456,A-42739|0){case 4:break c;case 0:break i;case 3:break r;case 2:break t;case 1:break ke;default:break Ye}}if((0|t)==43008)break Le;if((0|t)!=43264)break a;switch(r=4096,A-43310|0){case 0:break t;case 1:break H;default:break we}}if((0|t)<=70655){if((0|t)<=67839){if((0|t)<=65279){if((0|t)==43520)break Ie;if((0|t)==43776)break de;if((0|t)!=65024)break a;if(r=0,a=268435456,(0|(i=-2&A))==65040)break t;if((t=A-65042|0)>>>0<8)break GA;break Ce}if((0|t)==65280)break ue;if((0|t)==66304)break K;if((0|t)!=67584||(r=64,(0|A)!=67671))break a;break t}if((0|t)<=69375){if((0|t)==67840)break ae;if((0|t)==68096)break oe;if((0|t)!=68352)break a;switch(r=64,A-68410|0){case 0:case 1:case 2:case 3:case 4:case 5:case 95:case 96:case 97:case 98:break t;default:break a}}if((0|t)<=69887){if((0|t)==69376)break x;if((0|t)!=69632)break a;if(A-69703>>>0>=2)break ge;break H}if((0|t)==69888)break ce;if((0|t)!=70144)break a;if((t=A-70200|0)>>>0<=4)break ne;if((0|A)!=70313)break a;break H}if((0|t)<=74751){if((0|t)<=71423){if((0|t)==70656)break re;if((0|t)==70912)break ie;if((0|t)!=71168||(r=268435520,A-71233>>>0>=2))break a;break t}if((0|t)<=72703){if((0|t)==71424)break te;if((0|t)!=72192)break a;if(A-72258>>>0>=2)break j;break H}if((0|t)==72704)break X;if((0|t)!=73216||(r=268435520,A-73463>>>0>=2))break a;break t}if((0|t)<=93695){if((0|t)==74752)break O;if((0|t)==92672)break P;if((0|t)!=92928)break a;if(r=268435520,A-92983>>>0<2)break t;t=(0|A)==92996?268435520:0,t=(A=(0|A)==92985)?64:t;break F}if((0|t)<=121343){if((0|t)==93696)break z;if((0|t)!=113664||(r=268435520,a=-2147483648,(0|A)!=113823))break a;break t}if((0|t)==121344)break MA;if((0|t)!=125184)break a;r=((0|A)==125279)<<30,t=0,$=(A=(0|A)==125278)?536870912:r;break n}r=-1879048128,a=536870912;ve:{dA:{fA:{pA:{switch(A-33|0){default:switch(A-183|0){case 1:case 2:case 3:case 4:case 5:case 6:case 7:break ve;case 8:break fA;case 0:break pA;default:break dA}case 2:case 9:$=66;break u;case 11:return $=268435456,-2147483584;case 13:return $=-2147483648,-1879048128;case 25:return $=134217728,-2147483584;case 26:return $=67108864,-2147483584;case 1:case 6:break C;case 0:break t;case 3:case 4:case 5:case 7:case 8:case 10:case 12:case 14:case 15:case 16:case 17:case 18:case 19:case 20:case 21:case 22:case 23:case 24:case 27:case 28:case 29:break ve;case 30:}return $=1073741824,-1879048128}return $=0,134230016}$=1078984704;break u}if((0|A)==161)break Ue}break I}t=(0|A)==903,r=(A=(0|A)==894)?64:t?134217792:0,$=A?1073741824:t?67108864:0;break m}if((0|A)==1417)break Re;if((0|A)!=1475)break a;break se}$=1075838976;break o}$=-2143289344;break s}r=64,a=268435456;Be:switch(A-1548|0){case 15:break r;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 16:case 17:break a;case 0:break t;case 19:break Be;case 18:break H;default:break T}break c}if((t=A-1792|0)>>>0<6)break zA;if(r=64,a=134217728,(-2&A)==1798)break t;a=67108864;He:{Be:{Re:switch(A-1800|0){default:switch(A-2040|0){case 1:break He;case 0:break Be;default:break a}case 3:break a;case 0:break t;case 1:break Re;case 2:case 4:break se}$=1073741824;break A}$=268435456;break A}$=536870912;break s}t=(0|A)==2405,r=(A=(0|A)==2404)||t?268435520:0,$=A?-2143289344:t?16777216:0;break m}if(r=0,a=-2143289344,(0|A)!=3572)break a;break t}switch(r=64,A-3848|0){case 5:$=-2143289344;break A;case 6:$=16777216;break A;case 0:break t}if(A-3854>>>0<5)break t;if(r=0,a=268435456,(0|A)!=3860)break a;break t}if((t=A-4961|0)>>>0<4)break TA;if(r=64,a=134217728,A-4965>>>0<2)break t;t=(0|A)==4968,r=(A=(0|A)==4967)||t?268435520:0,$=A?1073741824:t?16777216:0;break m}if(A-5867>>>0>=3)break a;break t}if(r=268435520,A-5941>>>0<2)break t;if((A=A-6100|0)>>>0>=7)break a;t=e[(A=83240+(A<<3)|0)>>2];break h}if(r=268435520,(-4&A)!=6824)break a;break t}if(r=64,(0|A)==7005)break t;if(r=268435520,(0|t)!=7006)break a;break t}if(A-7227>>>0<2)break H;if(r=64,A-7229>>>0<3||(r=268435520,(-2&A)==7294))break t;if(r=4096,(0|A)!=7379)break a;break t}$=33554432;break u}if((-8&A)==8224)break I;if(A-8242>>>0<3)break t;if(A-8240>>>0<9)break I;if((t=A-8251|0)>>>0<21)break DA;break Ae}if((0|A)==11513){$=-2147483648;break o}if(r=0,a=1073741824,(-2&A)==11514)break t;if(a=-2147483648,(0|A)!=11518)break a;break t}switch(r=-1879048128,A-11822|0){case 4:case 6:$=268435456;break u;case 5:$=-2147483648;break u;case 7:$=67108864;break u;case 14:return $=-2147483648,-1879048128;case 19:return $=268435456,-2147483584;case 30:case 32:return $=0,-2147483584;case 0:break t}break I}if((0|A)==12349)break S;if((0|A)!=12539)break a;return $=0,16}return $=-2143289344,-1879048128}t=(0|A)==42239,r=(A=(0|A)==42238)?64:t?268435520:0,$=A?268435456:t?-2147483648:0;break m}Ye:switch(A-42509|0){case 1:break i;case 0:break t;case 2:break Ye;default:break a}break c}$=134217728;break A}if(A-43126>>>0<2)break H;if(r=268435520,A-43214>>>0>=2)break a;break t}if((0|A)==43463)break se;if(r=268435520,(-2&A)!=43464)break a;break t}if(A-43613>>>0<3)break H;if(r=64,(0|A)==43743)break t;if(r=268435520,(-2&A)!=43760)break a;break t}if(r=268435520,(0|A)!=44011)break a;break t}r=268435520,a=541065216;ue:switch(A-65281|0){case 1:case 6:return $=0,32;case 11:$=272629760;break A;case 13:$=-2143289344;break s;case 100:return $=0,16;case 25:$=138412032;break A;case 26:$=71303168;break A;case 30:$=1077936128;break s;case 96:break i;case 59:break k;case 0:break t;case 99:break ue;default:break a}$=268435456;break A}r=(t=(0|A)==66512)>>>26|0,t=(A=(0|A)==66463)?64:t<<6;break W}if(r=64,(0|A)!=67871)break a;break t}if(r=268435520,(-2&A)==68182)break t;if(r=64,A-68336>>>0>=6)break a;break t}if(r=268435520,A-69461>>>0>=5)break a;break t}if(r=64,A-69705>>>0<5)break t;if(r=268435520,A-69822>>>0>=4)break a;break t}if(A-69953>>>0<2)break H;if((t=A-70085|0)>>>0<=26)break Q;break Se}if((0|t)!=2)break H;break se}if(r=268435520,A-70731>>>0<2)break t;a=(t=(0|A)==70747)>>>26|0,t=(A=(0|A)==70733)?64:t<<6,$=A?268435456:a;break n}switch(r=268435520,(-2&A)-71106|0){case 0:break t;case 2:break g}if(r=8192,A-71110>>>0<3)break t;if(r=268435520,A-71113>>>0>=15)break a;break t}if(r=268435520,A-71484>>>0>=3)break a;break t}if(r=268435520,A-72347>>>0<2)break t;if(r=64,A-72353>>>0>=2)break a;break t}if(r=268435520,A-72769>>>0<2)break t;r=(t=(0|A)==72817)>>>26|0,t=(A=(0|A)==72771)?64:t<<6;break W}if(r=64,a=134217728,A-74865>>>0<2)break t;if(a=0,A-74864>>>0>=5)break a;break t}t=(0|A)==93848?268435520:0,t=(A=(0|A)==93847)?64:t;break F}if(r=268435520,(-2&A)==92782)break t;if(a=-2147483648,(0|A)!=92917)break a;break t}if((0|A)==1748)break i;break a}if(!(1<<t&100663555))break Se;break H}break se;case 23:g:{Q:{T:{P:{z:{O:{if((0|(t=-256&A))<=11775){if((0|t)<=8959){if(t)break O;break I}if((0|t)==8960)break z;if((0|t)==9984)break P;if((0|t)!=10496)break a;break U}if((0|t)<=64767){if((0|t)==11776)break T;if((0|t)!=12288)break a;if((A=A-12300|0)>>>0<=17&&(r=-2147483616,1<<A&131077))break t;break I}if((0|t)==64768)break Q;if((0|t)==65024)break g;if((0|t)!=65280)break a;if(r=32,(0|A)==65378)break t;break a}if((0|t)!=8192)break a;switch(r=-2147483616,A-8218|0){case 1:case 2:case 3:break a;case 0:case 4:break t}switch(A-8317|0){default:if((0|A)!=8261)break a;break I;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:break a;case 0:case 16:}break k}switch(r=-2147483520,A-8968|0){case 1:break a;case 0:case 2:break t}if((0|A)!=9001)break a;return $=0,-2139095040}if(r=-2147483520,(0|A)==10181)break t;break ye}return $=0,(0|A)==11842?-2147483616:-2147483648}if(r=-2147483648,(0|A)!=64831)break a;break t}r=(A=!(A-65089&-3))>>>27|0,A<<=5;break y;case 24:r=(A=A>>>0<256)>>>1|0,A<<=31;break y;case 25:g:{Q:{T:{P:{z:{O:{X:{j:{if((0|(t=-256&A))<=12287){if((0|t)<=767){if(!t)break j;if((0|t)!=512)break g;if(A-751>>>0>=17)break X;break f}if((0|t)==768)break O;if((0|t)!=7936)break g;switch(r=4096,A-8125|0){case 0:case 2:case 3:case 4:case 16:case 17:case 18:case 32:case 33:case 34:case 48:case 49:case 50:case 64:case 65:break t;default:break g}}if((0|t)<=43775){if((0|t)==12288)break z;if((0|t)!=42752||(r=4096,(-2&A)!=42784))break g;break t}if((0|t)==43776)break P;if((0|t)==65280)break T;if(r=0,a=78,(0|t)!=127744)break g;break t}switch(A-168|0){default:r=-2147479424;j:switch(A-94|0){case 0:break t;case 2:break j;default:break g}return $=0,-2147479552;case 0:case 7:break f;case 1:case 2:case 3:case 4:case 5:case 6:case 8:case 9:case 10:case 11:case 13:case 14:case 15:break g;case 12:case 16:}break f}if((0|A)==749|A-741>>>0<7)break f;if(A-706>>>0>=4)break Q;break f}if((A=A-885|0)>>>0>16||(r=4096,!(1<<A&98305)))break g;break t}if(r=67112960,A-12443>>>0>=2)break g;break t}if(r=4096,(0|A)!=43867)break g;break t}switch(r=4224,A-65342|0){default:if((0|A)!=65507)break g;break;case 0:break t;case 1:break g;case 2:}break f}if(r=4096,A-722>>>0<14)break t}break a;case 26:g:{Q:{T:{P:{z:{O:{if((0|(t=-256&A))<=9471){if((0|t)<=8447){if(t)break g;break I}if((0|t)==8448)break O;if((0|t)==8704)break z;if(r=-2147483648,(0|t)!=8960)break a;break t}if((0|t)<=10495){if((0|t)==9472)break P;if((0|t)==9728)break T;if((0|t)==9984)break I;break a}if((0|t)==10496)break Q;if((0|t)==10752)break I;if(r=-2147483648,(0|t)!=11008)break a;break t}if((0|A)==8472)return $=0,67108864;if((0|A)==8596)break Z;if(r=-2147483648,A>>>0<=8591)break a;break t}if(r=-2147483640,(0|A)==8722)break t;t=-2147483648,$=(A=A-8942>>>0<4)?33554432:0;break n}if(r=-2147483648,a=130,A-9723>>>0<2)break t;t=-2147483648,$=(A=A-9725>>>0<2)?134:0;break n}if(r=-2147483648,a=128,(0|A)==9839)break t;break I}if(r=-2147483648,a=130,(-2&A)==10548)break t;t=-2147483648,$=(A=(0|A)==10626)?134217728:0;break n}if((0|t)==8192)break Ke;break a;case 27:g:{Q:{T:{P:{z:{O:{X:{j:{te:{ie:{re:{ne:{ce:{ge:{x:{oe:{ae:{K:{ue:{de:{Ie:{we:{Le:{ke:{if((0|(t=-256&A))<=12287){if((0|t)<=9727){if((0|t)<=8959){if(!t)break ke;if((0|t)!=8448)break a;switch(r=0,a=130,A-8482|0){case 7:break k;case 0:break t;case 1:case 2:case 3:case 4:case 5:case 6:case 8:case 9:case 10:case 11:break de;case 12:break we;default:break Le}}if((0|t)==8960)break ue;if((0|t)==9216)break K;if((0|t)!=9472)break a;switch(r=-2147483520,(-2&A)-9632|0){case 0:break t;case 10:break Z;default:break ae}}if((0|t)<=11007){if((0|t)==9728)break oe;if((0|t)==9984)break x;if(r=-2147483648,(0|t)!=10240)break a;break t}if((0|t)==11008)break ge;if((0|t)==11776)break ce;if((0|t)!=12032)break a;if(r=1048576,A>>>0<12246)break t;switch(r=262144,(-2&A)-12272|0){case 0:break t;case 2:break g;default:break ne}}if((0|t)<=127999){if((0|t)<=127231){if((0|t)==12288)break re;if((0|t)==12800)break ie;if((0|t)!=126976)break a;t=(0|A)==127183,r=0,$=(A=(0|A)==126980)||t?134:128;break m}if((0|t)==127232)break te;if((0|t)==127488)break j;if((0|t)!=127744)break a;if(A>>>0>=127777)break X;break Y}if((0|t)<=128767){if((0|t)==128e3)break O;if((0|t)==128256)break z;if((0|t)!=128512)break a;if((t=A-128581|0)>>>0<11)break CA;break Ne}if((0|t)==128768)break P;if((0|t)==129280)break T;if((0|t)!=129536)break a;$=128;break o}t=(0|A)==174,r=-2147483648,$=(A=(0|A)==169)||t?130:0;break m}switch(A-8616|0){case 0:break I;case 1:case 2:break Ie;default:break de}}return $=0,67108864}return $=130,-2147483520}if(r=-2147483520,A-8597>>>0<5)break t;if(A-8604>>>0<18)break U;if((t=A-8624|0)>>>0<8)break EA;break Oe}if(A>>>0<8968)break I;if((0|(t=-2&A))==8986)break l;if(A-8972>>>0<20|A-8994>>>0<6)break I;if((0|A)==9e3)break Z;if(A-9003>>>0<81)break I;if(r=-2147483648,a=128,(0|A)==9096)break t;if(A-9085>>>0<30)break I;if(r=-2147483520,a=0,(0|t)==9140)break t;switch(A-9143|0){case 0:case 25:break t;case 24:break Z}if(A-9140>>>0<40)break I;if((0|A)==9186)break t;if((t=A-9193|0)>>>0<4)break l;switch(r=-2147483648,a=134,A-9200|0){case 0:case 3:break t}if(t>>>0<11)break Z;if(a=130,A-9208>>>0<3)break t;if(a=0,A>>>0<=9186)break a;break t}if(r=-2147483648,A-9216>>>0<75||(r=33792,a=130,(0|A)==9410)||(a=0,A-9398>>>0<26))break t;if(r=17408,A-9424>>>0>=26)break a;break t}if(A-9646>>>0<8)break U;if(a=130,(0|A)==9654)break t;if((-4&A)==9660)break U;switch(A-9664|0){case 0:break t;case 6:case 7:case 10:case 11:case 15:case 16:case 17:case 18:case 19:case 34:case 36:break U}return $=0,A-9703>>>0<6?-2147483520:-2147483648}oe:switch((-16&A)-9728>>>4|0){case 0:if(A>>>0<9733)break Z;r=-2147483520,a=128;ae:switch(A-9733|0){case 0:break t;case 9:break ae;case 1:break U;default:break G}break Z;case 2:if((t=A-9760|0)>>>0<11)break vA;if(r=-2147483648,a=130,A>>>0<=9773)break G;break t;case 3:if(r=-2147483648,a=130,A-9784>>>0>=3)break G;break t;case 4:switch(r=-2147483520,a=130,A-9792|0){case 0:case 2:break t}if(r=-2147483648,a=134,A>>>0<=9799)break G;break t;case 5:if(r=-2147483648,a=134,A>>>0<9812)break t;if(a=130,(0|A)!=9823)break G;break t;case 6:if((0|A)==9734)break U;if((0|A)==9824)return $=130,-2147483520;if(r=-2147483520,a=128,A-9825>>>0<2)break t;if((t=A-9827|0)>>>0<6)break BA;break be;case 8:if(r=-2147483648,A>>>0<=9861)break G;break t;case 10:switch(r=-2147483648,a=130,A-9888|0){case 1:break l;case 0:break t}if(a=134,(-2&A)!=9898)break G;break t;case 11:if(r=-2147483648,a=130,(-2&A)==9904)break t;if(a=134,A-9917>>>0>=2)break G;break t;case 12:if((-2&A)==9924)break l;r=-2147483648,a=130;ae:switch(A-9928|0){case 0:case 7:break t;case 6:break ae;default:break G}break l;case 14:t=(0|A)==9962,r=-2147483648,$=(A=(0|A)==9961)?130:t?134:128;break m;case 13:break wA;case 9:break kA;case 15:break oe;case 7:break xA;case 1:break yA;default:break G}if((0|A)==9972|A>>>0<9970)break Z;if(r=-2147483648,a=134,A>>>0<9974||(0|A)!=9974&&(a=130,A>>>0<9977))break t;if((A=A-9977|0)>>>0<5)break IA;break G}x:{oe:{ae:{K:{ue:switch((-16&A)-9984>>>4|0){case 0:if(r=0,a=130,(0|A)==9986||(a=128,A>>>0<9989)||(a=134,(0|A)==9989))break x;switch(a=150,(-2&A)-9994|0){case 0:break x;case 2:break ae}if(a=130,A-9992>>>0<6)break x;t=(0|A)==9999,r=0,a=(A=(0|A)==9998)?128:t?130:0;break x;case 1:if(r=0,a=128,A>>>0<10002)break x;if((A=A-10002|0)>>>0>11||(a=130,!(1<<A&2069)))break K;break x;case 2:t=(0|A)==10024,r=0,a=(A=(0|A)==10017)?130:t?134:0;break x;case 4:r=0,a=130;de:switch(A-10052|0){case 0:case 3:break x;case 8:case 10:break de;default:break K}a=134;break x;case 5:if(r=0,a=1073741958,A-10067>>>0<2)break x;a=(A=A-10069&-3)?0:536871046;break x;case 6:if((t=A-10082|0)>>>0<3)break oe;if(r=0,a=128,A>>>0<=10084)break K;break x;case 9:if(r=0,a=134,A-10133>>>0>=3)break K;break x;case 10:if(r=0,a=130,(0|A)!=10145)break K;break x;case 11:t=(0|A)==10175,r=0,a=(A=(0|A)==10160)||t?134:0;break x;case 3:break ue;default:break K}if(r=0,a=130,A-10035>>>0<2)break x}r=0,a=0;break x}a=146;break x}r=e[(A=83992+(t<<3)|0)>>2],a=e[A+4>>2]}return $=a,-2147483648|r}if(r=-2147483648,a=130,A-11013>>>0<3||(a=134,A-11035>>>0<2))break t;t=(0|A)==11093,r=-2147483648,$=(A=(0|A)==11088)||t?134:0;break m}if(A-11904>>>0>=26)break Q;return $=0,1048576}if(A-12276>>>0>=8)break a;break t}if((A=A-12306|0)>>>0>14||(r=-2147483648,!(1<<A&16387)))break a;break t}t=0,$=(A=A-12951&-3)?0:130;break n}if(r=0,a=128,(0|A)==127279)break t;if(A-127280>>>0<26||A-127312>>>0<26)return $=0,33792;if((t=A-127344|0)>>>0<=15&&(r=33792,a=130,1<<t&49155)||(r=33792,a=0,t>>>0<26))break t;if((0|A)==127374)break Y;if(r=0,a=134,A-127377>>>0<10)break t;if(a=102,A>>>0<=127461)break a;break t}r=0,a=134;j:{switch(A-127489|0){case 0:case 25:break t;case 1:break S;case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:case 16:case 17:case 18:case 19:case 20:case 21:case 22:case 23:case 24:break j}switch(A-127535|0){case 0:break t;case 8:break S;default:break j}}if((-2&A)==127568|A-127538>>>0<9)break t;if(a=128,A>>>0<=127583)break a;break t}if((0|A)==127777)break S;if(A-127789>>>0<9|A-127799>>>0<70)break Y;if((0|A)==127877)break q;if(A-127870>>>0<22)break Y;if(A-127780>>>0<112)break S;switch(r=0,a=130,A-127894|0){case 44:case 45:case 46:case 49:break q;case 0:case 1:case 3:case 4:case 5:break t}if(A-127904>>>0<42)break Y;if(a=150,(0|A)==127946||(a=146,A-127947>>>0<2))break t;if(A-127951>>>0<5)break Y;if(a=134,A-127968>>>0<17||(a=130,A-127902>>>0<83))break t;if((t=A-127987|0)>>>0<5)break bA;break Ze}switch(r=0,a=130,A-128063|0){case 0:case 2:break t}if((0|A)==128124|(-5&A)-128129>>>0<3|(0|A)==128110|A-128112>>>0<9||(-2&A)==128066|A-128102>>>0<4|A-128070>>>0<11)break q;switch(A-128253|0){case 1:$=128;break o;case 0:break S}if(a=150,(0|A)==128170)break t;break Y}if(A>>>0<128318)break Y;if(r=0,A>>>0<128326)break t;if(A-128329>>>0<2)break S;if(A-128331>>>0<4|A-128336>>>0<24)break Y;if(A-128367>>>0<2)break S;if((-2&A)==128372){$=146;break o}if(A-128371>>>0<7)break S;if(a=150,!(t=A-128378|0))break t;if((0|t)==13|A-128394>>>0<4)break S;if(a=146,(0|A)==128400||(a=150,A-128405>>>0<2))break t;switch(a=134,A-128420|0){case 0:break t;case 1:case 4:case 13:case 14:case 24:case 30:case 31:case 32:case 45:case 46:case 47:case 56:case 57:case 58:case 61:case 63:case 68:case 75:case 79:case 86:break S}A=A>>>0>128506,t=0;break p}if(r=0,a=128,A-128981>>>0>=4)break a;break t}if(A>>>0<129292)break a;if(A-129328>>>0<10)break q;switch(r=0,a=150,A-129304|0){case 35:break a;case 0:case 1:case 2:case 3:case 4:case 6:case 7:case 14:break t}if(A-129341>>>0<2)break q;if(a=0,(0|A)==129350||(a=198,(-4&A)==129456))break t;if((t=A-129461|0)>>>0<5)break mA;break hA}if(r=1048576,A-11931>>>0<89)break t;break a}return $=0,524288;case 29:return $=16777216,1073741825;case 28:break t;case 30:break jA;default:break a}return $=0,(0|A)==32?1073741825:1}if(!(1079>>>t&1))break fe;t=e[(A=81344+(t<<3)|0)>>2];break h}if(557553>>>t&1)break k;if((-4&A)!=8508)break Me;break t}if(!(207>>>t&1))break me;break d}if(r=1024,!(1017>>>t&1))break Ge;break t}if(r=1024,!(32895>>>t&1))break De;break t}if(r=1024,!(55>>>t&1))break Qe;break t}if(r=1024,!(3087>>>t&1))break Pe;break t}if((A=A-71453|0)>>>0>=15)break a;t=e[(A=82312+(A<<3)|0)>>2];break h}if(!(49023>>>t&1))break ze;t=e[(A=82432+(t<<3)|0)>>2];break h}if(!(514623>>>t&1))break _;t=e[(A=82664+(t<<3)|0)>>2];break h}t=e[(A=83160+(t<<3)|0)>>2];break h}if(r=268435520,!(197>>>t&1))break Fe;break t}t=e[(A=83208+(t<<3)|0)>>2];break h}if((A=A-6145|0)>>>0>=10)break a;t=e[(A=83296+(A<<3)|0)>>2];break h}if(!(1077711>>>t&1))break Ae;t=e[(A=83376+(t<<3)|0)>>2];break h}if(!(159>>>t&1))break Ce;t=e[(A=83544+(t<<3)|0)>>2];break h}if((A=A-121479|0)>>>0>=4)break a;t=e[(A=83608+(A<<3)|0)>>2];break h}if(!(195>>>t&1))break Oe;break U}if((A=A-9745|0)>>>0>=13)break G;t=e[(A=83640+(A<<3)|0)>>2];break h}if(1101>>>t&1)break Z;if(r=-2147483648,a=130,A>>>0<=9773)break G;break t}if(!(45>>>t&1))break be;t=e[(A=83744+(t<<3)|0)>>2];break h}if((A=A-9851|0)>>>0>=5)break G;t=e[(A=83792+(A<<3)|0)>>2];break h}if((A=A-9874|0)>>>0>=11)break G;t=e[(A=83832+(A<<3)|0)>>2];break h}if((A=A-9937|0)>>>0>=4)break G;t=e[(A=83920+(A<<3)|0)>>2];break h}t=e[(A=83952+(A<<3)|0)>>2];break h}if(!(23>>>t&1))break Ze;t=e[(A=84016+(t<<3)|0)>>2];break h}if(r=0,a=150,!(1991>>>t&1))break Ne;break t}if(a=150,27>>>t&1)break t}t=0,$=(A=A-129489>>>0<13)?150:134;break n}A=A-127992>>>0<3,t=0;break p}if(A-9837>>>0<2)break t}$=128;break u}if(A-8623>>>0<13)break I;if(A-8636>>>0<18)break U;if((t=A-8656|0)>>>0<22&&(a=0,3157995>>>t&1)||(r=-2147483648,a=0,A-8661>>>0<31))break t;break a}if(A>>>0<128592)break Y;if(r=0,a=0,A>>>0<128640)break t;if(!((t=A-128675|0)>>>0>29|!(1<<t&537788417)))break q;if(A>>>0<128710)break Y;if(a=150,(0|A)==128716)break t;if(A-128715>>>0<5)break S;if(A-128720>>>0<3)break Y;if(!((t=A-128736|0)>>>0>=10|!(575>>>t&1)))break S;if(a=134,A-128747>>>0<2)break t;switch(a=130,A-128752|0){case 0:case 3:break t}A=A-128756>>>0<6,t=0;break p}switch(r=-2147483648,A-8260|0){case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:break a;case 0:case 14:break t}if((0|(A=A-8315|0))!=16&&A)break a;return $=0,8}if(r=268435520,a=1073741824,(0|A)!=69955)break a;break t}if(A-8266>>>0<8)break I;if(r=-2147483640,(0|A)==8275)break t;if(r=-2147483648,A-8277>>>0>=10)break a;break t}$=542113792;break u}if(r=-2147483648,a=0,A-65093>>>0<2||(r=64,a=268435456,(0|i)==65104))break t;r=268435520,a=-2147483648;Ce:switch(A-65106|0){case 3:$=134217728;break A;case 5:$=536870912;break s;case 4:break c;case 2:break r;case 0:break t;case 15:case 22:break Ce;default:break a}break k}if((0|A)==2142)break se;if(r=64,A-2096>>>0<15)break t;break a}if(r=1024,A-72850>>>0<22)break t;if((A=A-72874|0)>>>0>=13)break a;t=e[(A=82560+(A<<3)|0)>>2];break h}if(r=4096,A-71103>>>0<2)break t;if(r=1024,(-2&A)!=71132)break a;break t}if((0|A)==70726)break f;if(r=4096,(-2&A)!=70850)break a;break t}if(r=4096,A-68325>>>0>=2)break a;break t}if((0|A)==6109)break f;if(r=4096,A-6089>>>0>=11)break a;break t}switch(A-3959|0){case 0:case 2:return $=0,8389632}if((0|t)==3968|A-3953>>>0<14)break d;if(!((t=A-3970|0)>>>0>=6|!(55>>>t&1)))break f;if(A-3981>>>0<11)break d;if(r=1024,A-3993>>>0<36)break t;if(r=4096,(0|A)!=4038)break a;break t}if(A-3655>>>0<6)break f;if(!((t=A-3761|0)>>>0>11|!(1<<t&3577)))break d;switch(r=1024,A-3661|0){case 1:break f;case 0:break t}if(r=4096,A-3784>>>0<5)break t;if(r=1024,(0|A)!=3789)break a;break t}if(A-1770>>>0<3)break t;if(r=1024,(0|A)!=1773)break a;break t}if((-2&A)==73028)break f;switch(r=1024,A-73104|0){default:if((0|A)!=73031)break a;break t;case 2:case 3:case 4:case 6:break a;case 0:case 1:case 5:break t;case 7:}break f}if((A=A-8492|0)>>>0>=30)break a;t=e[(A=81704+(A<<3)|0)>>2];break h}r=0;break N}a=(t=(-3&A)==43712)>>>20|0,r=t<<12}if((0|(s=-65536&A))==131072)break Ve;if((0|s)==65536)break he;if(t=r,i=a,s)break pe}if(A-13312>>>0<6582||A-19968>>>0<20976)break e;if(s=2048,A-63744>>>0<366)return $=i,2048|t;if(r=t,a=i,A-64112>>>0>=106)break pe;break R}if(s=2099200,A-183984>>>0<7473|A-178208>>>0<5762|A-177984>>>0<222|A-131072>>>0<42711||A-173824>>>0<4149||(s=2048,A-194560>>>0<542))break R;break pe}if(s=2048,A-110960>>>0<396|A-94208>>>0<6125|A-100352>>>0<755)break R}s=0}return $=a,r|s}if((-4&A)==8508)break k;if(r=128,!(A-8517>>>0<3)&&(r=16777344,(-2&A)!=8520))break a}$=a;break m}t=(A=A>>>0>131069)?65536:0,$=A?0:128;break n}if((0|A)==11776)break I}$=0;break o}$=0;break u}return $=0,4194304}return $=0,128}return $=0,16777344}return $=0,4096}return $=0,1024}return $=0,-2147483520}return $=0,A-10214>>>0<10?-2147483520:-2147483648}$=0;break s}$=0;break A}$=130;break o}$=130;break u}$=134;break o}return $=150,0}$=0;break n}$=A?0:a;break n}return $=t,A}return 131072}return $=0,16777216}$=A?0:r;break n}return $=a,A}return $=r,A}$=A?0:t;break m}return $=0,8192}$=e[A+4>>2];break n}$=A?8388608:0;break n}return r}return $=0,-2147483616}$=A?134:128}return t}$=134}return-2147483648}return 0}$=1073741824;break s}$=-2147483648}return 268435520}$=67108864}return 64}return $=i,2099200|t}function qe(A){var t,r=0,a=0,s=0,i=0,o=0;(t=Ct(8244))&&(e[t+328>>2]=2,g[132848]=0,e[t+684>>2]=0,e[t+688>>2]=0,e[t+320>>2]=0,e[t+324>>2]=0,g[t+268|0]=0,g[t+228|0]=0,e[t+8216>>2]=0,e[t+8220>>2]=0,e[t+224>>2]=104944,e[t+216>>2]=383,e[t+220>>2]=96,jA(t+344|0,0,292),e[t+8196>>2]=0,e[(r=t+8188|0)>>2]=0,e[r+4>>2]=0,e[t+8180>>2]=0,e[t+8184>>2]=0,g[t+460|0]=22,g[t+461|0]=129,g[t+466|0]=38,g[t+462|0]=38,g[t+463|0]=36,g[t+464|0]=22,g[t+465|0]=224,g[t+456|0]=22,g[t+457|0]=22,g[t+458|0]=44,g[t+459|0]=22,g[t+454|0]=46,g[t+455|0]=129,g[t+446|0]=22,g[t+447|0]=38,g[t+448|0]=28,g[t+449|0]=193,g[t+450|0]=38,g[t+451|0]=22,g[t+452|0]=46,g[t+453|0]=46,g[t+441|0]=129,g[t+442|0]=38,g[t+443|0]=22,g[t+444|0]=38,g[t+445|0]=193,e[t+332>>2]=104912,e[t+336>>2]=104916,e[t+340>>2]=105232,g[t+296|0]=18,g[t+297|0]=18,b[t+304>>1]=182,b[t+306>>1]=140,g[t+298|0]=20,b[t+308>>1]=220,b[t+310>>1]=220,b[t+312>>1]=220,g[t+299|0]=20,g[t+300|0]=20,b[t+314>>1]=240,g[t+301|0]=22,b[t+316>>1]=260,b[t+318>>1]=280,g[t+302|0]=22,g[t+303|0]=20,r=jA(t,0,212),e[r+200>>2]=20,e[r+192>>2]=25966,e[r+196>>2]=500,e[r+80>>2]=95,e[r+16>>2]=1,e[r+20>>2]=3,e[r+8>>2]=2,e[r+52>>2]=19,g[r+168|0]=3,e[r+92>>2]=2,e[r+72>>2]=4,e[r+40>>2]=115,e[r+44>>2]=95,e[r+140>>2]=105244,Qn(r,201),e[r+120>>2]=2,e[r+124>>2]=44,e[r+164>>2]=100,e[r+128>>2]=46,e[r+132>>2]=14,e[r+112>>2]=1227133512,e[r+116>>2]=49,e[r+104>>2]=1,a=e[26313],e[r+636>>2]=e[26312],e[r+640>>2]=a,a=e[26315],e[r+644>>2]=e[26314],e[r+648>>2]=a,a=e[26317],e[r+652>>2]=e[26316],e[r+656>>2]=a,a=e[26319],e[r+660>>2]=e[26318],e[r+664>>2]=a,a=e[26321],e[r+668>>2]=e[26320],e[r+672>>2]=a,a=e[26323],e[r+676>>2]=e[26322],e[r+680>>2]=a,a=l[104928]|l[104929]<<8,g[r+160|0]=a,g[r+161|0]=a>>>8,a=l[104924]|l[104925]<<8|l[104926]<<16|l[104927]<<24,g[r+156|0]=a,g[r+157|0]=a>>>8,g[r+158|0]=a>>>16,g[r+159|0]=a>>>24),s=qA(t+228|0,A),r=0;e:{A:if(a=l[0|A]){for(;r=(a<<24>>24)+(r<<8)|0,a=l[0|(A=A+1|0)];);r:{s:{i:{c:{o:{u:{l:{n:{p:{C:{m:{b:{h:{E:{w:{y:{L:{W:{V:{M:{B:{D:{F:{q:{Y:{Z:{S:{se:{H:{ye:{U:{d:{f:{$:{k:{ee:{I:{a:{We:{J:{t:{fe:{R:{pe:{he:{Ve:{le:{N:{Ee:{v:{Me:{_:{me:{Ge:{je:{De:{Qe:{Te:{Pe:{ze:{Fe:{Ce:{Ue:{Ae:{Se:{Ke:{Ne:{Oe:{G:{be:{if((0|r)<=28008){if((0|r)<=26464){if((0|r)<=25696){switch(r-24934|0){case 20:break n;case 1:case 2:case 3:case 4:case 5:case 6:case 9:case 10:case 11:case 14:case 15:case 16:case 17:case 18:case 19:break A;case 8:break _;case 12:break Fe;case 7:break Ce;case 0:break Ue;case 13:break be}switch(r-25189|0){case 1:case 3:case 4:case 5:case 6:case 7:case 8:case 10:case 11:case 12:case 13:break A;case 14:break fe;case 2:break Pe;case 0:break ze;case 9:break be}switch(r-25441|0){case 18:break y;case 0:break _;case 24:break Qe;default:break A}}switch(r-25964|0){case 1:case 4:case 5:case 6:break A;case 8:break Ee;case 9:break Me;case 7:break _;case 3:break me;case 2:break Ge;case 0:break Oe}switch(r-26209|0){case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 9:case 10:case 11:case 12:case 13:case 14:case 15:case 16:break A;case 17:break le;case 8:break N;case 0:break v}switch(r-25697|0){case 4:break je;case 0:break De;default:break A}}if((0|r)<=27488){switch(r-26729|0){case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 10:case 13:case 14:case 15:break A;case 16:break We;case 12:break J;case 11:break t;case 9:break fe;case 0:break R}switch(r-26977|0){case 1:case 2:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 15:case 16:case 17:break A;case 19:break k;case 18:break ee;case 3:break I;case 14:break a;case 0:break _}switch(r-26465|0){case 20:break R;case 13:break pe;case 0:case 3:break he;default:break A}}switch(r-27489|0){case 13:break p;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 12:case 15:case 16:case 17:case 18:case 19:case 21:case 22:case 23:break A;case 24:break se;case 20:break H;case 14:break ye;case 11:break U;case 10:break d;case 0:break f}switch(r-27745|0){case 19:break Z;case 0:break S;case 21:break Ke;default:break A}}if((0|r)<=29792){if((0|r)<=28768){switch(r-28009|0){case 3:break p;case 11:break q;case 2:break Y;case 1:case 4:case 5:case 6:case 7:case 8:case 12:case 13:case 14:case 15:break A;case 10:break I;case 9:break R;case 0:case 16:break Ae}switch(r-28258|0){case 0:break D;case 10:break F;case 1:case 2:case 4:case 5:case 6:case 7:case 8:case 9:break A;case 3:break R}switch(r-28525|0){case 0:break B;case 5:break R;default:break A}}if((0|r)<=29539){switch(r-28769|0){case 19:break V;case 11:break M;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 12:case 13:case 14:case 15:case 16:case 17:case 18:case 20:case 21:case 22:case 23:break A;case 0:break R;case 24:break Ae}switch(r-29295|0){case 6:break L;case 0:break W;case 1:case 2:case 3:case 4:case 5:break A}if((0|r)==29045)break Ae;break A}switch(r-29540|0){case 19:break C;case 18:break m;case 13:break b;case 8:break E;case 5:break w;case 7:break y;case 14:break fe;case 0:break o;default:break A}}if((0|r)>6514801)break G;if((0|r)<=30058)switch(r-29793|0){case 19:break l;case 17:break n;case 0:case 4:break p;case 13:break C;case 7:break Ae;default:break A}if((0|r)<=30312)switch(r-30059|0){case 0:break u;case 15:break Ae;case 7:break o;default:break A}if((0|r)==30313)break c;if((0|r)==31336)break i;if((0|r)!=6451321)break A}if(e[t+296>>2]=303174162,e[t+300>>2]=370545684,e[t+600>>2]=2432,e[t+8>>2]=0,e[t+12>>2]=65540,e[t+100>>2]=e[t+96>>2],A=e[25889],e[t+304>>2]=e[25888],e[t+308>>2]=A,A=e[25891],e[t+312>>2]=e[25890],e[t+316>>2]=A,us(t),g[t+345|0]=2|l[t+345|0],g[t+406|0]=16|l[t+406|0],g[t+407|0]=16|l[t+407|0],g[t+408|0]=16|l[t+408|0],g[t+409|0]=16|l[t+409|0],g[t+410|0]=16|l[t+410|0],g[t+411|0]=16|l[t+411|0],g[t+412|0]=16|l[t+412|0],g[t+413|0]=16|l[t+413|0],g[t+414|0]=16|l[t+414|0],g[t+415|0]=16|l[t+415|0],g[t+416|0]=16|l[t+416|0],g[t+417|0]=16|l[t+417|0],g[t+418|0]=16|l[t+418|0],g[t+419|0]=16|l[t+419|0],g[t+420|0]=16|l[t+420|0],g[t+456|0]=4|l[t+456|0],g[t+457|0]=4|l[t+457|0],e[t+112>>2]=613567144,e[t+104>>2]=16,(0|r)!=6451321)break e;e[t+104>>2]=1,e[t+108>>2]=512,r=6451321;break e}if((0|r)>7364975)break Se;if((0|r)>6840682)break Ne;if((0|r)==6514802)break Te;if((0|r)==6516078)break i;if((0|r)!=6779491)break A}if(e[t+600>>2]=896,e[t+328>>2]=8,e[t+296>>2]=336858127,e[t+300>>2]=353768980,e[t+332>>2]=103632,A=e[25905],e[t+304>>2]=e[25904],e[t+308>>2]=A,A=e[25907],e[t+312>>2]=e[25906],e[t+316>>2]=A,jA(t+344|0,0,256),g[t+388|0]=129,g[t+389|0]=129,g[t+390|0]=129,g[t+391|0]=129,g[t+420|0]=129,g[t+421|0]=129,g[t+422|0]=129,g[t+423|0]=129,g[t+360|0]=129,g[t+392|0]=129,g[t+393|0]=129,g[t+417|0]=129,g[t+418|0]=129,g[t+419|0]=129,g[t+420|0]=129,g[t+408|0]=6,g[t+409|0]=4,g[t+410|0]=6,g[t+411|0]=6,g[t+412|0]=6,g[t+413|0]=193,g[t+414|0]=6,g[t+415|0]=6,g[t+406|0]=6,g[t+407|0]=129,g[t+398|0]=4,g[t+399|0]=193,g[t+400|0]=6,g[t+401|0]=193,g[t+402|0]=6,g[t+403|0]=4,g[t+404|0]=4,g[t+405|0]=4,g[t+394|0]=4,g[t+395|0]=4,g[t+396|0]=4,g[t+397|0]=193,e[t+44>>2]=130,e[t+8>>2]=2,e[t+12>>2]=6,e[t+16>>2]=0,e[t+20>>2]=2,e[t+104>>2]=264,e[t+108>>2]=6146,g[t+391|0]=193,g[t+389|0]=193,g[t+390|0]=193,g[t+421|0]=193,e[t+100>>2]=e[t+96>>2],g[t+416|0]=4|l[t+416|0],(0|r)!=6779491)break e;e[t+40>>2]=1,r=6779491;break e}if((0|r)==6840683)break Ve;if((0|r)==6972015)break $;if((0|r)!=7107687)break A}e[t+296>>2]=134875662,e[t+300>>2]=252968960,e[t+328>>2]=5,g[t+169|0]=1,e[t+132>>2]=33,e[t+104>>2]=99336,e[t+8>>2]=0,e[t+12>>2]=262182,A=e[26069],e[t+304>>2]=e[26068],e[t+308>>2]=A,A=e[26071],e[t+312>>2]=e[26070],e[t+316>>2]=A;break e}if((0|r)<=7564649){if((0|r)==7364976)break _;if((0|r)==7435619)break Ae;if((0|r)!=7563374)break A;e[t+148>>2]=1,e[t+112>>2]=24,e[t+104>>2]=1,e[t+100>>2]=e[t+96>>2],r=7563374;break e}if((0|r)==7564650)break h;if((0|r)==7959909)break i;if((0|r)!=1885958500)break A}e[t+104>>2]=0;break e}e[t+4>>2]=48,e[t+8>>2]=0,e[t+144>>2]=1,e[t+104>>2]=16779472,e[t+32>>2]=1,e[t+24>>2]=1,A=e[25881],e[t+304>>2]=e[25880],e[t+308>>2]=A,A=e[25883],e[t+312>>2]=e[25882],e[t+316>>2]=A,g[t+465|0]=64&l[t+465|0]|129,r=24934;break e}e[t+600>>2]=4608,e[t+296>>2]=303173650,e[t+300>>2]=303174162,e[t+8>>2]=0,e[t+12>>2]=36,e[t+104>>2]=1024,e[t+100>>2]=e[t+96>>2],e[t+40>>2]=1,A=e[25865],e[t+304>>2]=e[25864],e[t+308>>2]=A,A=e[25867],e[t+312>>2]=e[25866],e[t+316>>2]=A,r=24941;break e}for(e[t+600>>2]=1536,e[t+224>>2]=0,e[t+216>>2]=1631,e[t+220>>2]=1536,e[t+104>>2]=2884720,e[t+328>>2]=7,e[t+40>>2]=1,J=r=J-16|0,e[r+12>>2]=-1,A=89684;s=Gt(r+12|0,A),(0|(a=e[r+12>>2]))>=33&&(g[0|(i=(t+a|0)-1192|0)]=1|l[0|i]),A=A+s|0,a;);for(e[r+12>>2]=-1,A=89743;s=Gt(r+12|0,A),(0|(a=e[r+12>>2]))>=33&&(g[0|(i=(t+a|0)-1192|0)]=2|l[0|i]),A=A+s|0,a;);for(e[r+12>>2]=-1,A=89795;s=Gt(r+12|0,A),(0|(a=e[r+12>>2]))>=33&&(g[0|(i=(t+a|0)-1192|0)]=4|l[0|i]),A=A+s|0,a;);for(e[r+12>>2]=-1,A=89941;s=Gt(r+12|0,A),(0|(a=e[r+12>>2]))>=33&&(g[0|(i=(t+a|0)-1192|0)]=16|l[0|i]),A=A+s|0,a;);for(e[r+12>>2]=-1,A=90045;s=Gt(r+12|0,A),(0|(a=e[r+12>>2]))>=33&&(g[0|(i=(t+a|0)-1192|0)]=32|l[0|i]),A=A+s|0,a;);for(e[r+12>>2]=-1,A=90045;s=Gt(r+12|0,A),(0|(a=e[r+12>>2]))>=33&&(g[0|(i=(t+a|0)-1192|0)]=8|l[0|i]),A=A+s|0,a;);for(e[r+12>>2]=-1,A=90045;s=Gt(r+12|0,A),(0|(a=e[r+12>>2]))>=33&&(g[0|(i=(t+a|0)-1192|0)]=64|l[0|i]),A=A+s|0,a;);J=r+16|0,r=24946;break e}e[t+600>>2]=1056,e[t+12>>2]=34,e[t+216>>2]=1118,e[t+220>>2]=1072,jA(t+344|0,0,256),g[t+406|0]=4,g[t+366|0]=4,g[t+367|0]=4,g[t+369|0]=4,g[t+370|0]=4,g[t+371|0]=4,g[t+372|0]=4,g[t+361|0]=4,g[t+362|0]=4,g[t+363|0]=4,g[t+364|0]=4,g[t+373|0]=4,g[t+380|0]=4,g[t+381|0]=4,g[t+382|0]=4,g[t+383|0]=4,g[t+375|0]=4,g[t+376|0]=4,g[t+377|0]=4,g[t+378|0]=4,g[t+384|0]=4,g[t+360|0]=129,e[t+328>>2]=6,e[t+296>>2]=134744588,e[t+300>>2]=286261248,e[t+40>>2]=1,e[t+8>>2]=0,e[t+104>>2]=1032,e[t+108>>2]=66,A=e[25885],e[t+304>>2]=e[25884],e[t+308>>2]=A,A=e[25887],e[t+312>>2]=e[25886],e[t+316>>2]=A,r=25189;break e}xs(t),e[t+328>>2]=6,e[t+56>>2]=2,e[t+36>>2]=263,e[t+40>>2]=1074,e[t+124>>2]=32,e[t+104>>2]=184554728,e[t+8>>2]=2,g[t+386|0]=64&l[t+386|0]|129,r=25191;break e}e[t+12>>2]=262182,e[t+40>>2]=1,r=6514802;break e}e[t+328>>2]=14,e[t+296>>2]=303173393,e[t+300>>2]=336986112,e[t+104>>2]=1024,e[t+16>>2]=0,e[t+20>>2]=2,e[t+8>>2]=2,e[t+12>>2]=22,e[t+44>>2]=120,A=e[25893],e[t+304>>2]=e[25892],e[t+308>>2]=A,A=e[25895],e[t+312>>2]=e[25894],e[t+316>>2]=A,g[t+463|0]=64&l[t+463|0]|129,g[t+465|0]=64&l[t+465|0]|129,r=25465;break e}e[t+8>>2]=0,e[t+104>>2]=184618072,e[t+32>>2]=1,A=e[26101],e[t+304>>2]=e[26100],e[t+308>>2]=A,A=e[26103],e[t+312>>2]=e[26102],e[t+316>>2]=A,g[t+465|0]=64&l[t+465|0]|129,r=25697;break e}e[t+296>>2]=336860180,e[t+300>>2]=336991764,e[t+8>>2]=0,e[t+104>>2]=16846872,e[t>>2]=8,e[t+4>>2]=48,e[t+80>>2]=87,e[t+32>>2]=1,e[t+36>>2]=256,e[t+40>>2]=2,A=e[25897],e[t+304>>2]=e[25896],e[t+308>>2]=A,A=e[25899],e[t+312>>2]=e[25898],e[t+316>>2]=A,g[t+465|0]=64&l[t+465|0]|129,r=25701;break e}e[t+132>>2]=33,e[t+104>>2]=16779328,e[t+8>>2]=0,e[t+12>>2]=8,e[t+204>>2]=101,e[t+60>>2]=2,e[t+40>>2]=2,A=e[25901],e[t+304>>2]=e[25900],e[t+308>>2]=A,A=e[25903],e[t+312>>2]=e[25902],e[t+316>>2]=A,g[t+441|0]=64|l[t+441|0],g[t+445|0]=64|l[t+445|0],g[t+449|0]=64|l[t+449|0],g[t+455|0]=64|l[t+455|0],g[t+461|0]=64|l[t+461|0],g[t+465|0]=64|l[t+465|0],r=25966;break e}e[t+328>>2]=4,e[t+296>>2]=336858640,e[t+300>>2]=353768980,e[t+104>>2]=16782344,e[t+20>>2]=2,e[t+12>>2]=22,e[t+4>>2]=2,e[t+8>>2]=2,e[t+332>>2]=103640,A=e[25997],e[t+304>>2]=e[25996],e[t+308>>2]=A,A=e[25999],e[t+312>>2]=e[25998],e[t+316>>2]=A,r=25967;break e}e[t+296>>2]=269422096,e[t+300>>2]=370545684,e[t+104>>2]=86017320,e[t+108>>2]=6144,e[t+16>>2]=0,e[t+20>>2]=2,e[t+8>>2]=2,e[t+12>>2]=534,e[t+100>>2]=e[t+96>>2],e[t+44>>2]=120,A=e[25913],e[t+304>>2]=e[25912],e[t+308>>2]=A,A=e[25915],e[t+312>>2]=e[25914],e[t+316>>2]=A;_:{me:{if((0|r)<=26976){if((0|r)==24942)break me;if((0|r)!=25441)break _;e[t+12>>2]=566,e[t+336>>2]=103664,r=25441;break e}if((0|r)!=26977){if((0|r)!=7364976)break _;e[t+8>>2]=3,e[t+12>>2]=310,r=7364976;break e}e[t+104>>2]=85984264,r=26977;break e}e[t+104>>2]=153093416,e[t+108>>2]=2048,e[t+140>>2]=103676,r=24942;break e}e[t+40>>2]=2;break e}e[t+296>>2]=303173648,e[t+300>>2]=303174162,e[t+104>>2]=3147080,e[t+12>>2]=65792,e[t+84>>2]=1,A=e[25921],e[t+304>>2]=e[25920],e[t+308>>2]=A,A=e[25923],e[t+312>>2]=e[25922],e[t+316>>2]=A,r=25973;break e}e[t+600>>2]=1536,e[t+216>>2]=1740,e[t+220>>2]=1568,e[t+104>>2]=96,e[t+224>>2]=103696,e[t+340>>2]=103872,e[t+40>>2]=1,r=26209;break e}e[t+328>>2]=5}e[t+104>>2]=86024,e[t+164>>2]=130,g[t+465|0]=64&l[t+465|0]|129;break e}e[t+296>>2]=303173650,e[t+300>>2]=303174162,e[t+8>>2]=3,e[t+12>>2]=36,e[t+144>>2]=2,e[t+104>>2]=118658312,e[t+28>>2]=1,e[t+100>>2]=e[t+96>>2],A=e[25865],e[t+304>>2]=e[25864],e[t+308>>2]=A,A=e[25867],e[t+312>>2]=e[25866],e[t+316>>2]=A,g[t+465|0]=64&l[t+465|0]|129,r=26226;break e}b[t+170>>1]=257,e[t+148>>2]=1,e[t+12>>2]=2,r=6840683;break e}e[t+144>>2]=2,e[t+104>>2]=2098176,e[t+8>>2]=0,e[t+12>>2]=32,e[t+40>>2]=3,e[t+28>>2]=1;break e}e[t+8>>2]=3,e[t+100>>2]=e[t+96>>2],r=26478;break e}e[t+328>>2]=18,e[t+296>>2]=320081425,e[t+300>>2]=353768980,e[t+600>>2]=2304,e[t+112>>2]=84648,e[t+104>>2]=16,e[t+8>>2]=6,e[t+12>>2]=65540,e[t+100>>2]=e[t+96>>2],A=e[25973],e[t+304>>2]=e[25972],e[t+308>>2]=A,A=e[25975],e[t+312>>2]=e[25974],e[t+316>>2]=A;R:{pe:{he:{if((0|r)<=28529){if((0|r)==26485)break he;if((0|r)!=28261)break R;A=e[25861],e[t+304>>2]=e[25860],e[t+308>>2]=A,A=e[25863],e[t+312>>2]=e[25862],e[t+316>>2]=A,e[t+296>>2]=320017171,e[t+300>>2]=320017171,e[t+132>>2]=22,e[t+112>>2]=-1431655768,e[t+108>>2]=32768|e[t+108>>2],us(t);break e}if((0|r)==28530)break pe;if((0|r)!=28769)break R;e[t+600>>2]=2560,us(t);break e}A=e[25861],e[t+304>>2]=e[25860],e[t+308>>2]=A,A=e[25863],e[t+312>>2]=e[25862],e[t+316>>2]=A,e[t+600>>2]=2688,e[t+296>>2]=320017171,e[t+300>>2]=320017171,e[t+8>>2]=2,us(t);break e}e[t+600>>2]=2816}us(t);break e}g[0|s]=104,g[s+1|0]=98,g[s+2|0]=115,g[s+3|0]=0,(0|r)!=29554?(A=e[26093],e[t+304>>2]=e[26092],e[t+308>>2]=A,A=e[26095],e[t+312>>2]=e[26094],e[t+316>>2]=A):(A=e[25977],e[t+304>>2]=e[25976],e[t+308>>2]=A,A=e[25979],e[t+312>>2]=e[25978],e[t+316>>2]=A),e[t+328>>2]=3,e[t+296>>2]=336859409,e[t+300>>2]=353768980,b[t+168>>1]=261,e[t+8>>2]=0,e[t+12>>2]=16,e[t+144>>2]=1,e[t+184>>2]=1056,e[t+104>>2]=33572172,e[t+108>>2]=330,e[t+36>>2]=3,g[t+465|0]=64&l[t+465|0]|129,g[t+458|0]=64&l[t+458|0]|129;break e}e[t+104>>2]=17990912,e[t+8>>2]=3,e[t+12>>2]=36,r=26740;break e}e[t+328>>2]=3,e[t+296>>2]=320016657,e[t+300>>2]=353768980,e[t+124>>2]=32,e[t+128>>2]=44,e[t+104>>2]=186758144,e[t+12>>2]=1081398,e[t+16>>2]=2,e[t+4>>2]=32,e[t+8>>2]=0,e[t+116>>2]=899,e[t+120>>2]=1,g[t+169|0]=1,e[t+76>>2]=2,A=e[25981],e[t+304>>2]=e[25980],e[t+308>>2]=A,A=e[25983],e[t+312>>2]=e[25982],e[t+316>>2]=A,g[t+465|0]=64&l[t+465|0]|129,Qn(t,3),r=26741;break e}for(e[t+600>>2]=1328,e[t+8>>2]=3,A=e[25985],e[t+304>>2]=e[25984],e[t+308>>2]=A,A=e[25987],e[t+312>>2]=e[25986],e[t+316>>2]=A,s=jA(t+344|0,0,256),g[t+429|0]=129,g[t+416|0]=129,g[t+403|0]=129,g[t+399|0]=129,g[t+400|0]=129,g[t+397|0]=129,g[t+393|0]=129,a=103952,i=50,o=50;g[0|(A=s+o|0)]=2|l[0|A],A=s+l[a+1|0]|0,g[0|A]=2|l[0|A],A=s+l[a+2|0]|0,g[0|A]=2|l[0|A],o=l[0|(a=a+3|0)],A=103952,(0|a)!=103982;);for(a=t+344|0;g[0|(s=a+i|0)]=4|l[0|s],s=a+l[A+1|0]|0,g[0|s]=4|l[0|s],s=a+l[A+2|0]|0,g[0|s]=4|l[0|s],i=l[0|(A=A+3|0)],(0|A)!=103982;);g[t+168|0]=6,e[t+104>>2]=5128,g[t+413|0]=4|l[t+413|0];break e}e[t+328>>2]=4,e[t+296>>2]=336858640,e[t+300>>2]=353768980,e[t+104>>2]=16782440,e[t+20>>2]=2,e[t+12>>2]=22,e[t+4>>2]=2,e[t+8>>2]=2,e[t+332>>2]=104e3,A=e[25997],e[t+304>>2]=e[25996],e[t+308>>2]=A,A=e[25999],e[t+312>>2]=e[25998],e[t+316>>2]=A,r=26991;break e}e[t+296>>2]=303174160,e[t+300>>2]=353768980,e[t+104>>2]=16781320,e[t+144>>2]=2,e[t+8>>2]=2,e[t+12>>2]=22,A=e[26005],e[t+304>>2]=e[26004],e[t+308>>2]=A,A=e[26007],e[t+312>>2]=e[26006],e[t+316>>2]=A;break e}for(e[t+8>>2]=0,e[t+12>>2]=16,e[t+56>>2]=2,e[t+28>>2]=17,A=e[26009],e[t+304>>2]=e[26008],e[t+308>>2]=A,A=e[26011],e[t+312>>2]=e[26010],e[t+316>>2]=A,A=0,a=t+344|0;g[0|(s=A+a|0)]=231&l[0|s],g[0|(s=a+(1|A)|0)]=231&l[0|s],g[0|(s=a+(2|A)|0)]=231&l[0|s],g[0|(s=a+(3|A)|0)]=231&l[0|s],(0|(A=A+4|0))!=256;);e[t+104>>2]=2280,e[t+108>>2]=2,e[t+608>>2]=104048,g[t+451|0]=16|l[t+451|0],g[t+456|0]=16|l[t+456|0],g[t+459|0]=16|l[t+459|0],g[t+460|0]=16|l[t+460|0],g[t+450|0]=8|l[t+450|0],g[t+462|0]=8|l[t+462|0],g[t+458|0]=8|l[t+458|0],g[t+465|0]=64&l[t+465|0]|129;break e}e[t+296>>2]=269618961,e[t+300>>2]=370546196,e[t+12>>2]=131110,e[t+144>>2]=2,e[t+104>>2]=184559112,e[t+108>>2]=8192,e[t+16>>2]=0,e[t+20>>2]=2,e[t+4>>2]=1,e[t+8>>2]=2,e[t+100>>2]=e[t+96>>2],e[t+140>>2]=103676,e[t+68>>2]=2,e[t+56>>2]=1,e[t+44>>2]=130,e[t+28>>2]=2,A=e[26025],e[t+304>>2]=e[26024],e[t+308>>2]=A,A=e[26027],e[t+312>>2]=e[26026],e[t+316>>2]=A,g[t+465|0]=64&l[t+465|0]|129,r=26996;break e}e[t+4>>2]=524,e[t+8>>2]=2,e[t+196>>2]=368,e[t+104>>2]=0,e[t+336>>2]=104128,e[t- -64>>2]=1,A=e[26029],e[t+304>>2]=e[26028],e[t+308>>2]=A,A=e[26031],e[t+312>>2]=e[26030],e[t+316>>2]=A,g[t+465|0]=64&l[t+465|0]|129,r=6972015;break e}for(e[t+296>>2]=303174162,e[t+300>>2]=370545684,A=e[25873],e[t+304>>2]=e[25872],e[t+308>>2]=A,A=e[25875],e[t+312>>2]=e[25874],e[t+316>>2]=A,s=jA(t+344|0,0,256),g[t+431|0]=1,g[t+429|0]=1,g[t+411|0]=1,g[t+405|0]=1,g[t+400|0]=1,g[t+396|0]=1,g[t+392|0]=1,A=104160,a=49;g[0|(a=a+s|0)]=4|l[0|a],a=s+l[A+1|0]|0,g[0|a]=4|l[0|a],a=s+l[A+2|0]|0,g[0|a]=4|l[0|a],a=l[0|(A=A+3|0)],(0|A)!=104193;);e[t+600>>2]=4256,e[t+8>>2]=0,e[t+12>>2]=16,g[t+168|0]=7,e[t+132>>2]=32,g[t+392|0]=128|l[t+392|0],g[t+396|0]=128|l[t+396|0],g[t+400|0]=128|l[t+400|0],g[t+405|0]=128|l[t+405|0],g[t+411|0]=128|l[t+411|0],g[t+429|0]=128|l[t+429|0],g[t+431|0]=128|l[t+431|0],e[t+188>>2]=1056,e[t+192>>2]=29301,e[t+104>>2]=19964960;break e}for(e[t+600>>2]=1056,s=jA(t+344|0,0,256),g[t+393|0]=1,g[t+365|0]=1,g[t+360|0]=1,g[t+545|0]=1,g[t+529|0]=1,g[t+391|0]=1,g[t+389|0]=1,g[t+390|0]=1,g[t+387|0]=1,g[t+379|0]=1,g[t+374|0]=1,g[t+368|0]=1,g[t+489|0]=1,g[t+487|0]=1,g[t+398|0]=1,A=104224,a=17;g[0|(a=a+s|0)]=4|l[0|a],a=s+l[A+1|0]|0,g[0|a]=4|l[0|a],a=s+l[A+2|0]|0,g[0|a]=4|l[0|a],a=l[0|(A=A+3|0)],(0|A)!=104251;);g[t+360|0]=128|l[t+360|0],g[t+365|0]=128|l[t+365|0],g[t+393|0]=128|l[t+393|0],g[t+368|0]=128|l[t+368|0],g[t+374|0]=128|l[t+374|0],g[t+379|0]=128|l[t+379|0],g[t+387|0]=128|l[t+387|0],g[t+389|0]=128|l[t+389|0],g[t+390|0]=128|l[t+390|0],g[t+391|0]=128|l[t+391|0],g[t+529|0]=128|l[t+529|0],g[t+545|0]=128|l[t+545|0],g[t+489|0]=128|l[t+489|0],g[t+487|0]=128|l[t+487|0],g[t+398|0]=128|l[t+398|0],A=e[26055],e[t+312>>2]=e[26054],e[t+316>>2]=A,A=e[26053],e[t+304>>2]=e[26052],e[t+308>>2]=A,e[t+296>>2]=353636370,e[t+300>>2]=336925972,e[t+200>>2]=0,e[t+8>>2]=7,e[t+12>>2]=2097184,g[t+168|0]=2,e[t+104>>2]=50176,e[t+84>>2]=1,Qn(t,3);break e}e[t+296>>2]=320017171,e[t+300>>2]=320017171,e[t+104>>2]=184618072,e[t+8>>2]=12,e[t+12>>2]=32,A=e[25861],e[t+304>>2]=e[25860],e[t+308>>2]=A,A=e[25863],e[t+312>>2]=e[25862],e[t+316>>2]=A,r=27500;break e}e[t+184>>2]=42752,e[t+600>>2]=4352,jA(t+344|0,0,256),g[t+456|0]=1,g[t+457|0]=1,g[t+458|0]=1,g[t+459|0]=1,g[t+449|0]=1,g[t+450|0]=1,g[t+451|0]=1,g[t+452|0]=1,g[t+453|0]=1,g[t+454|0]=1,g[t+455|0]=1,g[t+456|0]=1,g[t+441|0]=1,g[t+442|0]=1,g[t+443|0]=1,g[t+444|0]=1,g[t+445|0]=1,g[t+446|0]=1,g[t+447|0]=1,g[t+448|0]=1,g[t+460|0]=65,g[t+461|0]=65,g[t+532|0]=32,g[t+527|0]=32,g[t+519|0]=32,g[t+515|0]=32,g[t+349|0]=32,g[t+350|0]=32,g[t+346|0]=32,e[t+132>>2]=20,e[t+112>>2]=286331152,e[t+104>>2]=1024,e[t+108>>2]=16384,e[t+40>>2]=1,e[t+8>>2]=8,g[t+458|0]=65,g[t+453|0]=65,g[t+447|0]=65,g[t+448|0]=65,g[t+443|0]=65,g[t+444|0]=65,r=27503;break e}e[t+328>>2]=10,e[t+296>>2]=336859666,e[t+300>>2]=353768980,g[t+168|0]=2,e[t+104>>2]=263264,e[t+8>>2]=7,A=e[26065],e[t+304>>2]=e[26064],e[t+308>>2]=A,A=e[26067],e[t+312>>2]=e[26066],e[t+316>>2]=A,r=27509;break e}e[t+104>>2]=1,r=27513;break e}e[t+116>>2]=5e3,e[t+104>>2]=16777216,e[t+24>>2]=1,e[t+16>>2]=0,e[t+20>>2]=2,e[t+8>>2]=2,e[t+12>>2]=32,e[t+328>>2]=5,r=27745;break e}e[t+116>>2]=5e3,e[t+104>>2]=99336,e[t+108>>2]=256,e[t+24>>2]=1,e[t+16>>2]=0,e[t+20>>2]=2,e[t+8>>2]=2,e[t+12>>2]=32,e[t+328>>2]=5,r=27764;break e}e[t+328>>2]=6,e[t+296>>2]=336859409,e[t+300>>2]=353768980,e[t+600>>2]=1056,e[t+104>>2]=2114600,e[t+108>>2]=138,e[t+8>>2]=4,e[t+632>>2]=104288,e[t+604>>2]=104288,A=e[26093],e[t+304>>2]=e[26092],e[t+308>>2]=A,A=e[26095],e[t+312>>2]=e[26094],e[t+316>>2]=A,r=28011;break e}e[t+328>>2]=4,e[t+104>>2]=1,e[t+8>>2]=2,e[t+36>>2]=256,r=28020;break e}e[t+4>>2]=48,e[t+8>>2]=0,e[t+12>>2]=128,e[t+104>>2]=2169880,e[t+32>>2]=1,e[t+36>>2]=256,e[t+24>>2]=1,e[t+136>>2]=85767,A=e[26097],e[t+304>>2]=e[26096],e[t+308>>2]=A,A=e[26099],e[t+312>>2]=e[26098],e[t+316>>2]=A,g[t+465|0]=64&l[t+465|0]|129,r=28268;break e}e[t+8>>2]=0,e[t+104>>2]=71752,A=e[26101],e[t+304>>2]=e[26100],e[t+308>>2]=A,A=e[26103],e[t+312>>2]=e[26102],e[t+316>>2]=A,g[t+465|0]=64&l[t+465|0]|129,r=28258;break e}e[t+296>>2]=336858898,e[t+300>>2]=370546196,e[t+104>>2]=1088,e[t+108>>2]=512,e[t+8>>2]=2,e[t+12>>2]=524310,A=e[26105],e[t+304>>2]=e[26104],e[t+308>>2]=A,A=e[26107],e[t+312>>2]=e[26106],e[t+316>>2]=A,r=28525;break e}e[t+328>>2]=3,e[t+296>>2]=320015633,e[t+300>>2]=353768980,g[t+168|0]=7,e[t+8>>2]=2,e[t+12>>2]=6,e[t+104>>2]=20488,e[t+108>>2]=192,e[t+36>>2]=9,e[t+60>>2]=260,A=e[26109],e[t+304>>2]=e[26108],e[t+308>>2]=A,A=e[26111],e[t+312>>2]=e[26110],e[t+316>>2]=A,g[t+465|0]=64&l[t+465|0]|129,r=28780;break e}for(e[t+296>>2]=353569552,e[t+300>>2]=353768980,e[t+116>>2]=5e3,e[t+104>>2]=33570920,e[t+108>>2]=14336,e[t+8>>2]=3,e[t+12>>2]=139286,e[t+100>>2]=e[t+96>>2],A=e[26113],e[t+304>>2]=e[26112],e[t+308>>2]=A,A=e[26115],e[t+312>>2]=e[26114],e[t+316>>2]=A,g[t+465|0]=64&l[t+465|0]|129,A=0,a=t+344|0;g[0|(s=A+a|0)]=253&l[0|s],g[0|(s=a+(1|A)|0)]=253&l[0|s],g[0|(s=a+(2|A)|0)]=253&l[0|s],g[0|(s=a+(3|A)|0)]=253&l[0|s],(0|(A=A+4|0))!=256;);g[t+442|0]=2|l[t+442|0],g[t+443|0]=2|l[t+443|0],g[t+444|0]=2|l[t+444|0],g[t+446|0]=2|l[t+446|0],g[t+447|0]=2|l[t+447|0],g[t+450|0]=2|l[t+450|0],g[t+451|0]=2|l[t+451|0],g[t+453|0]=2|l[t+453|0],g[t+454|0]=2|l[t+454|0],g[t+456|0]=2|l[t+456|0],g[t+457|0]=2|l[t+457|0],g[t+459|0]=2|l[t+459|0],g[t+460|0]=2|l[t+460|0],g[t+462|0]=2|l[t+462|0],g[t+464|0]=2|l[t+464|0],g[t+466|0]=2|l[t+466|0],e[t+144>>2]=2,e[t+68>>2]=2;break e}e[t+296>>2]=303172879,e[t+300>>2]=353768980,e[t+328>>2]=3,e[t+8>>2]=3,e[t+12>>2]=262,e[t+104>>2]=16805928,e[t+108>>2]=30,A=e[26117],e[t+304>>2]=e[26116],e[t+308>>2]=A,A=e[26119],e[t+312>>2]=e[26118],e[t+316>>2]=A,r=29295;break e}fn(t),r=29301;break e}for(e[t+328>>2]=3,e[t+296>>2]=336859153,e[t+300>>2]=353768980,b[t+168>>1]=261,e[t+8>>2]=0,e[t+12>>2]=22,e[t+124>>2]=0,e[t+128>>2]=44,e[t+104>>2]=16794624,e[t+108>>2]=128,e[t+36>>2]=3,e[t+60>>2]=4,A=e[25869],e[t+304>>2]=e[25868],e[t+308>>2]=A,A=e[25871],e[t+312>>2]=e[25870],e[t+316>>2]=A,(0|r)==25459&&(e[t+108>>2]=136),g[t+465|0]=64&l[t+465|0]|129,g[t+458|0]=64&l[t+458|0]|129,A=0,a=t+344|0;g[0|(s=A+a|0)]=223&l[0|s],g[0|(s=a+(1|A)|0)]=223&l[0|s],g[0|(s=a+(2|A)|0)]=223&l[0|s],g[0|(s=a+(3|A)|0)]=223&l[0|s],(0|(A=A+4|0))!=256;);g[t+442|0]=32|l[t+442|0],g[t+444|0]=32|l[t+444|0],g[t+447|0]=32|l[t+447|0],g[t+450|0]=32|l[t+450|0],g[t+452|0]=32|l[t+452|0],g[t+453|0]=32|l[t+453|0],g[t+454|0]=32|l[t+454|0],g[t+458|0]=32|l[t+458|0],g[t+462|0]=32|l[t+462|0],g[t+463|0]=32|l[t+463|0],g[t+466|0]=32|l[t+466|0],g[t+441|0]=32|l[t+441|0],g[t+445|0]=32|l[t+445|0],g[t+449|0]=32|l[t+449|0],g[t+455|0]=32|l[t+455|0],g[t+461|0]=32|l[t+461|0],g[t+465|0]=32|l[t+465|0];break e}for(e[t+296>>2]=303174162,e[t+300>>2]=370545684,e[t+600>>2]=3456,g[t+169|0]=1,e[t+8>>2]=0,e[t+12>>2]=22,e[t+100>>2]=e[t+96>>2],A=e[25873],e[t+304>>2]=e[25872],e[t+308>>2]=A,A=e[25875],e[t+312>>2]=e[25874],e[t+316>>2]=A,jA(t+344|0,0,256),g[t+365|0]=1,g[t+366|0]=1,g[t+357|0]=1,g[t+358|0]=1,g[t+359|0]=1,g[t+360|0]=1,g[t+361|0]=1,g[t+362|0]=1,g[t+363|0]=1,g[t+364|0]=1,g[t+349|0]=1,g[t+350|0]=1,g[t+351|0]=1,g[t+352|0]=1,g[t+353|0]=1,g[t+354|0]=1,g[t+355|0]=1,g[t+356|0]=1,A=74,a=74;g[344+(s=t+a|0)|0]=1|l[s+344|0],g[s+345|0]=1|l[s+345|0],g[s+346|0]=1|l[s+346|0],(0|(a=a+3|0))!=116;);for(;g[344+(a=A+t|0)|0]=2|l[a+344|0],g[a+345|0]=2|l[a+345|0],g[a+346|0]=2|l[a+346|0],(0|(A=A+3|0))!=116;);for(a=26;g[344+(A=t+a|0)|0]=4|l[A+344|0],g[A+345|0]=4|l[A+345|0],g[A+346|0]=4|l[A+346|0],g[A+347|0]=4|l[A+347|0],g[A+348|0]=4|l[A+348|0],(0|(a=a+5|0))!=71;);e[t+112>>2]=84648,e[t+104>>2]=270589952,e[t+108>>2]=65536,e[t+40>>2]=1,e[t+204>>2]=e[t+600>>2]+74;break e}e[t+8>>2]=2,e[t+12>>2]=32,e[t+328>>2]=3,e[t+124>>2]=32,e[t+104>>2]=16864280,e[t+108>>2]=256,e[t+68>>2]=2,e[t+36>>2]=259,e[t+40>>2]=118,e[t+28>>2]=1,g[t+458|0]=128|l[t+458|0],r=29548;break e}e[t+296>>2]=370544658,e[t+300>>2]=370546196,e[t+164>>2]=130,e[t+8>>2]=0,e[t+12>>2]=86,e[t+104>>2]=87064,g[t+169|0]=1,e[t+152>>2]=3,A=e[26121],e[t+304>>2]=e[26120],e[t+308>>2]=A,A=e[26123],e[t+312>>2]=e[26122],e[t+316>>2]=A,g[t+465|0]=64&l[t+465|0]|129,r=7564650;break e}e[t+296>>2]=269487120,e[t+300>>2]=320148500,e[t+8>>2]=3,e[t+12>>2]=278,e[t+144>>2]=2,e[t+104>>2]=32872,A=e[26125],e[t+304>>2]=e[26124],e[t+308>>2]=A,A=e[26127],e[t+312>>2]=e[26126],e[t+316>>2]=A,g[t+465|0]=64&l[t+465|0]|129,r=29553;break e}e[t+296>>2]=336859152,e[t+300>>2]=353768980,e[t+8>>2]=0,e[t+144>>2]=1,e[t+104>>2]=6408,A=e[26129],e[t+304>>2]=e[26128],e[t+308>>2]=A,A=e[26131],e[t+312>>2]=e[26130],e[t+316>>2]=A,g[t+465|0]=64&l[t+465|0]|129,r=29558;break e}e[t+296>>2]=320015376,e[t+300>>2]=353768980,g[t+168|0]=4,e[t+12>>2]=22,e[t+4>>2]=1,e[t+8>>2]=2,e[t+104>>2]=1248,e[t+100>>2]=e[t+96>>2],A=e[26133],e[t+304>>2]=e[26132],e[t+308>>2]=A,A=e[26135],e[t+312>>2]=e[26134],e[t+316>>2]=A;break e}e[t+296>>2]=303174162,e[t+300>>2]=370545684,g[t+169|0]=1,e[t+8>>2]=0,e[t+12>>2]=22,e[t+112>>2]=5288,e[t+100>>2]=e[t+96>>2],A=e[25877],e[t+304>>2]=e[25876],e[t+308>>2]=A,A=e[25879],e[t+312>>2]=e[25878],e[t+316>>2]=A;p:switch(r-29793|0){default:if((0|r)!=27502){if((0|r)!=28012)break r;A=e[26137],e[t+304>>2]=e[26136],e[t+308>>2]=A,A=e[26139],e[t+312>>2]=e[26138],e[t+316>>2]=A,e[t+600>>2]=3328,e[t+296>>2]=320017171,e[t+300>>2]=320017171,e[t+104>>2]=2098176,e[t+108>>2]=131072,e[t+8>>2]=13;break r}e[t+104>>2]=1,e[t+600>>2]=3200;break r;case 4:break p;case 1:case 2:case 3:break r;case 0:break s}e[t+104>>2]=1,e[t+108>>2]=524288,e[t+600>>2]=3072;break r}e[t+328>>2]=10,e[t+296>>2]=353636370,e[t+300>>2]=336925972,g[t+173|0]=1,e[t+8>>2]=7,e[t+12>>2]=32,g[t+168|0]=2,e[t+84>>2]=1,A=e[26141],e[t+304>>2]=e[26140],e[t+308>>2]=A,e[t+104>>2]=(0|r)==24954?2118920:2114824,A=e[26143],e[t+312>>2]=e[26142],e[t+316>>2]=A;break e}xs(t),e[t+296>>2]=303173650,e[t+300>>2]=303174162,e[t+104>>2]=2131208,e[t+8>>2]=3,e[t+12>>2]=32,A=e[25865],e[t+304>>2]=e[25864],e[t+308>>2]=A,A=e[25867],e[t+312>>2]=e[25866],e[t+316>>2]=A,r=29812;break e}fn(t),r=30059;break e}e[t+112>>2]=21160,e[t+104>>2]=16,e[t+600>>2]=1536,e[t+40>>2]=1;break e}e[t+296>>2]=269488144,e[t+300>>2]=370546198,e[t+8>>2]=0,e[t>>2]=33,e[t+148>>2]=1,e[t+104>>2]=12615688,e[t+16>>2]=2,e[t+100>>2]=e[t+96>>2],e[t+632>>2]=104592,e[t+604>>2]=104592,A=e[26145],e[t+304>>2]=e[26144],e[t+308>>2]=A,A=e[26147],e[t+312>>2]=e[26146],e[t+316>>2]=A,r=30313;break e}if(e[t+296>>2]=370544662,e[t+300>>2]=370546198,e[t+8>>2]=3,e[t+12>>2]=2,e[t+148>>2]=1,e[t+184>>2]=12544,b[t+170>>1]=257,e[t+176>>2]=1,g[t+172|0]=1,e[t>>2]=33,e[t+4>>2]=0,e[t+100>>2]=e[t+96>>2],A=e[26225],e[t+304>>2]=e[26224],e[t+308>>2]=A,A=e[26227],e[t+312>>2]=e[26226],e[t+316>>2]=A,(0|r)!=7959909)break e;e[t+112>>2]=24,e[t+104>>2]=1,e[t+108>>2]=1048576,r=7959909;break e}A=e[25873],e[t+304>>2]=e[25872],e[t+308>>2]=A,A=e[25875],e[t+312>>2]=e[25874],e[t+316>>2]=A,e[t+600>>2]=2944,e[t+104>>2]=2097152,e[t+108>>2]=262144,e[t+48>>2]=1}us(t),g[t+422|0]=2|l[t+422|0];break e}e[t+40>>2]=1}return e[t+212>>2]=r,8&(A=e[t+104>>2])&&(e[t+124>>2]=46,e[t+128>>2]=44),4&A&&(e[t+124>>2]=0),t}function dA(A){var t=0,r=0,a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0,MA=0;e:{r=e[32538],e[47354]=0,e[47568]=0,e[49828]=0,e[47569]=0,g[199328]=0,e[49827]=0,e[49845]=0,g[190280]=0,g[190268]=1,e[47202]=0,e[49573]=0,e[49846]=0,g[199304]=0,g[199388]=0,e[33691]=0,e[33285]=0,e[33708]=1,e[33709]=1,e[33288]=0,t=e[33730],e[33712]=e[33729],e[33713]=t,t=e[33732],e[33714]=e[33731],e[33715]=t,t=e[33734],e[33716]=e[33733],e[33717]=t,t=e[33736],e[33718]=e[33735],e[33719]=t,t=e[33738],e[33720]=e[33737],e[33721]=t,t=e[33740],e[33722]=e[33739],e[33723]=t,t=e[33742],e[33724]=e[33741],e[33725]=t,e[33726]=e[33743],g[134784]=0,g[134824]=0,g[134772]=0,g[134760]=0,e[33284]=-1,e[33692]=0,e[32525]=0,e[47201]=e[33717],e[47200]=e[33718],Va(),e[34438]=0,e[34437]=0,t=e[33730],e[34048]=e[33729],e[34049]=t,t=e[33732],e[34050]=e[33731],e[34051]=t,t=e[33734],e[34052]=e[33733],e[34053]=t,t=e[33736],e[34054]=e[33735],e[34055]=t,t=e[33738],e[34056]=e[33737],e[34057]=t,t=e[33740],e[34058]=e[33739],e[34059]=t,t=e[33742],e[34060]=e[33741],e[34061]=t,e[34062]=e[33743];A:{r:{if(1&r){if(e[e[32539]>>2]=0,e[47569]|e[49845]|e[49827])break r;break A}if(e[e[32539]>>2]=0,!(e[49845]|e[49827]||e[47569]))break A}g[190280]=1}e[49828]=0,t=268436735;A:if(!(!e[34391]|!e[34388])&&(e[47204]=0,e[47203]=0,e[47199]=0,e[34439]=0,(e[47192]||!(t=i0(86228)))&&((t=e[33283])||((t=Ct(16))&&(e[t>>2]=0,e[t+4>>2]=0,e[t+8>>2]=0,e[t+12>>2]=0),e[33283]=t),i=268439807,(r=e[e[47192]+328>>2])>>>0>20|!e[129104+(r<<3)>>2]?t=i:(A?(i=4,s=HA(A)+1|0):(i=2,s=0),e[t+8>>2]=i,e[t>>2]=A,e[t+12>>2]=e[129108+(r<<3)>>2],e[t+4>>2]=A?A+s|0:0,t=0),!t))){me(0);r:{for(;;){e[34436]=0,A=e[34391],e[51290]=A,e[54046]=A+e[34390],e[50767]<=102399&&(e[50767]=102400);s:if(!((o=e[51290])>>>0>=ue[54046]))for(;;){if((((0|(A=(s=e[50757])-(c=e[50758])|0))<=0?A+170|0:A)-171|0)>=-1){if((0|(A=e[54731]))<=0)break s;i=0,e[50763]=0,e[50762]=0,e[50765]=2147483647,A=l[218920]?e[54732]:A;i:{for(;;){if(r=A-1|0,e[54732]=r,(0|A)<=0)break i;if(e[51290]=o+1,t=(A=e[51293])+1|0,e[51293]=(0|t)<=5499?t:0,t=(A=z(e[50755],b[205184+(A<<1)>>1]))>>8,g[0|o]=t,s=e[51290],e[51290]=s+1,g[0|s]=A>>>16,(s=e[50756])?(A=r,(r=e[s+4>>2])&&(ct[0|r](t<<16>>16),A=e[54732])):A=r,s=(r=e[51292])+1|0,e[51292]=s,b[205184+(r<<1)>>1]=t,(0|s)>=5500&&(e[51292]=0),o=e[51290],!(ue[54046]>=o+2>>>0))break}i=1}g[218920]=i;break s}A=e[4+(r=216192+(s<<4)|0)>>2];i:{c:{o:{u:{l:{n:{p:{C:{m:{b:{h:{E:{w:{y:{L:{W:switch((255&(t=e[r>>2]))-1|0){case 9:break o;case 7:break u;case 10:break l;case 11:break n;case 13:break p;case 0:break C;case 1:break m;case 2:break b;case 3:break h;case 6:break E;case 5:break w;case 4:break y;case 15:break L;case 8:break W;default:break c}if(!e[50759])break c;t=e[r+12>>2],r=e[r+8>>2],e[50768]=0,o=r||99232,e[50766]=o,e[50769]=A?2097152/(0|A)|0:0,A=e[50971],s=(0|z(A,e[50788]))/50|0,r=z(s-A|0,-18),A=((0|(A=e[50785]))>=101?101:A)-e[50790]|0,t=(i=r+((0|z(e[50970],l[((0|A)>0?A:0)+105680|0]))/128|0)|0)+((0|z(s,(0|(A=t>>16))<(0|(r=65535&t))?A:r))/2|0)|0,e[50770]=t,A=(i+((0|z(s,(0|A)>(0|r)?A:r))/2|0)|0)-t|0,e[50771]=A,r=l[0|o],e[33072]=e[50976],e[50767]=t+(z(A,r)>>8);break c}ct[e[e[50756]>>2]](A,e[r+8>>2]),xt(A);break c}if(l[218920]||(e[54731]=e[54731]-A),e[50781]=100,e[50773]=0,_(),e[50763]=0,e[50762]=0,e[50765]=2147483647,!A)break c;for(A=l[218920]?e[54732]:A;;){if(r=A-1|0,e[54732]=r,(0|A)<=0)break c;if(A=e[51290],e[51290]=A+1,s=(t=e[51293])+1|0,e[51293]=(0|s)<=5499?s:0,s=A,t=(A=z(e[50755],b[205184+(t<<1)>>1]))>>8,g[0|s]=t,s=e[51290],e[51290]=s+1,g[0|s]=A>>>16,(s=e[50756])?(A=r,(r=e[s+4>>2])&&(ct[0|r](t<<16>>16),A=e[54732])):A=r,s=(r=e[51292])+1|0,e[51292]=s,b[205184+(r<<1)>>1]=t,(0|s)>=5500&&(e[51292]=0),s=1,!(ue[54046]>=e[51290]+2>>>0))break}break i}for(e[50773]=0,e[54731]=e[54729],_(),t=e[r+12>>2],s=e[r+8>>2],l[218920]?A=e[54733]:e[54734]=0,c=t>>8,i=255&t,e[50762]=0,e[50763]=0;;){if(r=A-1|0,e[54733]=r,(0|A)<=0)break c;if(t=(A=e[54734])+1|0,i?o=z(i,g[A+s|0]):(o=l[A+s|0]|g[t+s|0]<<8,t=A+2|0),e[54734]=t,f=(A=e[51293])+1|0,e[51293]=f,t=(0|(A=(0|(A=((0|z(c,z(e[33037],z(e[33038],o))>>10))/32|0)+(z(e[50755],b[205184+(A<<1)>>1])>>8)|0))<=-32768?-32768:A))>=32767?32767:A,(0|f)>=5500&&(e[51293]=0),g[e[51290]]=t,g[e[51290]+1|0]=t>>>8,(o=e[50756])?(A=r,(r=e[o+12>>2])&&(ct[0|r](t<<16>>16),A=e[54733])):A=r,r=e[51290],e[51290]=r+2,f=(o=e[51292])+1|0,e[51292]=f,b[205184+(o<<1)>>1]=(0|z(t,3))/4,(0|f)>=5500&&(e[51292]=0),!(ue[54046]>=r+4>>>0))break}s=1;break i}t=e[r+12>>2],s=A>>>16|0,e[50777]=s,A&=65535,e[50773]=A,i=255&t,e[50774]=i,e[50775]=t>>8,i||(e[50777]=s<<1,e[50773]=A<<1),e[50778]=0,e[50776]=0,e[50772]=e[r+8>>2];break c}e[50773]=0}if(e[54731]=e[54729],t=e[50759],l[218920]){if(!t)break c}else{if(!t)break c;for(i=e[r+12>>2],o=e[r+8>>2],r=A>>16,e[55912]=255&r,g[218960]=1,e[55908]=0,67108864&A&&(e[55908]=3,e[55909]=e[110496+(r>>>6&12)>>2]),134217728&A&&(e[55908]=4,e[55909]=e[110512+(r>>>6&12)>>2]),A&=65504;;){if((0|c)!=(0|(s=(0|(r=s+1|0))<=169?r:0)))if((0|(r=e[216192+(s<<4)>>2]))!=3){if(r-5>>>0>1)continue}else g[218960]=0;break}for(e[55913]=e[50762],A=(A=A+32&131008)||64,e[50763]=A+e[50763],e[55684]=z(b[101997],7800)+(M[102024]<<8)<<8,e[55704]=z(b[101998],9e3)+(M[102025]<<8)<<8,T=e[50980],a=+(0|A),I=+(A>>>2|0),A=0;(0|A)!=7&&(s=b[218+(f=(r=A<<1)+t|0)>>1]<<8,c=z(A,80)+222176|0,f=b[f+164>>1],E=s+z(f,b[2+(r+o|0)>>1])<<8,e[c>>2]=E,p=+(0|E),D[c+16>>3]=p,D[c+48>>3]=16*(+(s+z(f,b[2+(r+i|0)>>1])<<8)-p)/I),s=(r=z(A,80))+222176|0,f=b[182+(E=t+(A<<1)|0)>>1],Z=z(f,l[18+(c=A+o|0)|0])<<6,e[s+4>>2]=Z,p=+(0|Z),D[s+24>>3]=p,L=s,s=A+i|0,D[L+56>>3]=64*(+(z(f,l[s+18|0])<<6)-p)/a,(0|A)>(0|T)|A>>>0>5||(f=r+222176|0,Z=b[E+200>>1],E=z(Z,l[c+26|0])<<10,e[f+8>>2]=E,p=+(0|E),D[f+32>>3]=p,L=f- -64|0,f=Z<<10,D[L>>3]=64*(+(0|z(f,l[s+26|0]))-p)/a,A>>>0<=2?(r=r+222176|0,c=z(f,l[c+32|0]),e[r+12>>2]=c,p=+(0|c),D[r+40>>3]=p,D[r+72>>3]=64*(+(0|z(f,l[s+32|0]))-p)/a):e[r+222188>>2]=E),(0|(A=A+1|0))!=8;);}for(;;){if(A=e[50762],!l[218960]&(0|A)==e[50763])break c;b:{h:{if(63&A){if(7&A)break b;E:if(!((0|(r=e[54736]))<=0||(0|(t=e[54735]))<=0))for(s=e[50826],A=1;;){if(e[(o=(i=A<<2)+s|0)>>2]=e[o>>2]+e[i+203312>>2],A>>>0>28|(0|A)>=(0|r))break E;if(i=(0|A)<(0|t),A=A+1|0,!i)break}if((0|(A=e[33073]))>255)break b;e[33073]=A+1;break b}if(A)if(e[50759]){if(r=e[50768]+e[50769]|0,e[50768]=r,r=(t=e[50766])?z(l[t+((0|(r>>=8))>=127?127:r)|0],e[50771])>>8:0,e[55911]=e[55911]+e[55915],t=(0|(t=e[55914]))<=23551?t:0,e[55914]=t+e[50761],t=(r+e[50770]|0)+z(e[33072],l[110528+(t>>6)|0]-128|0)|0,e[50767]=t,!(s=e[51291])&(0|(r=s?s<<12:t))>102399||(t=(0|r)<=102400?102400:r,e[50767]=t),(0|A)!=e[55913]){if(A=0,!((0|(c=e[50980]))<0)){for(;o=z(A,80),a=D[48+(r=o+222176|0)>>3]+D[r+16>>3],D[r+16>>3]=a,I=D[r+56>>3]+D[r+24>>3],D[r+24>>3]=I,p=D[r- -64>>3]+D[r+32>>3],D[r+32>>3]=p,s=Ne(a)<2147483648?~~a:-2147483648,e[r>>2]=s,s=Ne(p)<2147483648?~~p:-2147483648,e[r+8>>2]=s,i=Ne(I)<2147483648?~~I:-2147483648,e[r+4>>2]=(0|i)>0?i:0,(0|A)>2||(a=D[72+(r=o+222176|0)>>3]+D[r+40>>3],D[r+40>>3]=a,s=Ne(a)<2147483648?~~a:-2147483648),e[o+222188>>2]=s,(0|c)>=(0|(A=A+1|0)););if((0|A)>=8)break h}for(;(0|A)!=7&&(r=z(A,80)+222176|0,a=D[r+48>>3]+D[r+16>>3],D[r+16>>3]=a,s=Ne(a)<2147483648?~~a:-2147483648,e[r>>2]=s),r=z(A,80)+222176|0,a=D[r+56>>3]+D[r+24>>3],D[r+24>>3]=a,s=Ne(a)<2147483648?~~a:-2147483648,e[r+4>>2]=(0|s)>0?s:0,(0|(A=A+1|0))!=8;);}}else t=e[50767];else e[50826]=218976,e[54742]=0,e[54736]=ds(e[50767]<<4,218976,0),t=e[50767],e[54737]=890/(t>>12),e[54739]=(0|z(e[50781],z(e[50779],t>>8)))/8e4}if(e[55906]=t>>11,e[54735]=e[54736],e[55904]=z(e[50760],t>>7),e[55905]=e[50754]/(t>>12),r=1^(A=e[54742]),e[54742]=r,e[50826]=z(A,1600)+218976,e[54736]=ds(t<<4,z(r,1600)+218976|0,1),!(!(t=e[50759])|!e[51022]))for(I=D[25430],p=D[25429],A=1;e[272+(r=t+(A<<2)|0)>>2]&&(s=e[r+308>>2],r=z(A,40)+203456|0,m=Ga(I*+b[2+(z(A,80)+222176|0)>>1]),m*=a=Pt(p*+(0|s)),m+=m,D[r+8>>3]=m,a*=-a,D[r+16>>3]=a,D[r>>3]=1-m-a),(0|(A=A+1|0))!=9;);}if(i=e[50762]+1|0,e[50762]=i,o=(A=e[50765])+e[55904]|0,e[50765]=o,(0|o)<0&(0|A)>0){if(f=e[55905],t=e[50800]+((0|f)/-2|0)|0,e[55907]=t,(0|(E=e[50763]))<(0|i))break c;if(T=e[54738]+1|0,e[54738]=T,c=e[50767],!((0|(A=(r=e[50980])+1|0))>8)&&(s=c<<3,1&r&&(e[203264+(A<<2)>>2]=(1+(e[z(A,80)+222176>>2]/(0|s)|0)|0)/2,A=r+2|0),(0|r)!=7))for(;r=203264+(A<<2)|0,Z=z(A,80)+222176|0,e[r>>2]=(1+(e[Z>>2]/(0|s)|0)|0)/2,e[r+4>>2]=(1+(e[Z+80>>2]/(0|s)|0)|0)/2,(0|(A=A+2|0))!=9;);A=(0|z(e[50781],z(e[50779],c>>8)))/8e4|0,e[54739]=A;b:if(!((0|(r=e[55908]))<=0)){switch(r-3|0){case 0:if((E-i|0)>=f<<1)break b;e[55908]=2,A=(0|z(e[55909],A))/256|0,e[54739]=A;break b;case 1:e[55908]=2,A=(0|z(e[55909],A))/256|0,e[54739]=A;break b}e[55908]=r-1}(r=e[55910])&&(s=A,A=e[55911]>>8,A=(0|z(s,l[r+((0|A)>=127?127:A)|0]))/128|0,e[54739]=A),(0|(r=e[e[32972]+92>>2]))>7||(r=15&(s=l[e[55912]+(106336+(r<<3)|0)|0]),(s=s>>>4|0)&&((0|s)!=15?(0|T)%(0|s)|0||(e[54739]=(0|z(A,r))/16):(e[55912]=0,e[54739]=(0|z(A,r))/16)))}else t=e[55907];if(i=t+1|0,e[55907]=i,r=o>>>16|0,s=0,!((0|i)<0|(0|i)>=e[50799])){if(!((0|(A=(t=e[50980])+1|0))>8)){if(c=1&(o=8-t|0),(0|t)!=7)for(f=-2&o,t=0;s=z(e[203216+(E=4+(o=A<<2)|0)>>2],b[106400+(z(r,e[E+203264>>2])>>>4&4094)>>1])+(z(e[o+203216>>2],b[106400+(z(r,e[o+203264>>2])>>>4&4094)>>1])+s|0)|0,A=A+2|0,(0|f)!=(0|(t=t+2|0)););c&&(s=z(e[203216+(A<<=2)>>2],b[106400+(z(r,e[A+203264>>2])>>>4&4094)>>1])+s|0)}s=z(l[i+132160|0],(0|s)/e[55906]|0)}if(A=1,(0|(i=e[54737]))<=0)t=r;else for(o=e[50826],t=r;s=z(e[o+(A<<2)>>2],b[106400+((65504&t)>>>4|0)>>1])+s|0,t=t+r|0,(0|i)>=(0|(A=A+1|0)););if((0|(i=e[54735]))>=(0|A))for(o=e[50826];s=s-z(e[o+(A<<2)>>2],b[106400+((65504&t)>>>4|0)>>1])|0,t=t+r|0,(0|i)>=(0|(A=A+1|0)););if(i=(0|(A=e[54728]))==64?s:z(A,s>>6),e[51022]){if(e[50759])for(r=It(e[33209],0,1103515245,0),A=$,A=Rn(r=r+12345|0,A=r>>>0<12345?A+1|0:A),e[33209]=A,I=+((16383&A)- -8192|0),t=e[50759],o=0,A=1;(s=e[272+(t+(A<<2)|0)>>2])&&(c=e[z(A,80)+222180>>2],r=z(A,40)+203456|0,a=D[r+32>>3],p=D[r+24>>3],D[r+32>>3]=p,a=a*D[r+16>>3]+(D[r>>3]*I+p*D[r+8>>3]),D[r+24>>3]=a,r=Ne(a)<2147483648?~~a:-2147483648,o=z(r,z(s,c>>14))+o|0),(0|(A=A+1|0))!=9;);else o=0;i=i+o|0}t=0,(0|(A=e[50776]))>=e[50773]||(r=e[50778],t=e[50772],(o=e[50774])?(s=A+1|0,e[50776]=s,A=z(o,g[t+(A+r|0)|0])):(o=l[0|(t=t+(A+r|0)|0)],t=g[t+1|0],s=A+2|0,e[50776]=s,A=o|t<<8),t=(0|z(z(A,e[50780])>>10,e[50775]))/32|0,(0|(A=e[50777]))>(r+s|0)||(e[50778]=r+((0|z(A,3))/-4|0))),r=(A=e[51293])+1|0,e[51293]=r,A=((z(e[54739],i>>8)>>13)+t|0)+(z(e[50755],b[205184+(A<<1)>>1])>>8)|0,(0|r)>=5500&&(e[51293]=0),r=e[33073];b:{h:{if((0|(t=z(r,A)))>=8388608){if((0|r)>=(0|(o=8388608/(0|A)|0)))break h;break b}if((0|t)>-8388353||(0|r)<(0|(o=-8388608/(0|A)|0)))break b}r=o-1|0,e[33073]=r,t=z(A,r)}if(A=e[51290],e[51290]=A+1,r=A,A=t>>8,g[0|r]=A,r=e[51290],e[51290]=r+1,g[0|r]=t>>>16,(r=e[50756])&&(r=e[r+8>>2])&&ct[0|r](A<<16>>16),t=(r=e[51292])+1|0,e[51292]=t,b[205184+(r<<1)>>1]=A,(0|t)>=5500&&(e[51292]=0),!(ue[54046]>=e[51290]+2>>>0))break}s=1;break i}e[50773]=0}e[54731]=e[54729],s=1,c=65535&A,t=l[218920],o=e[r+8>>2],i=e[r+12>>2],a=0,p=0,J=r=J+-64|0,f=e[50759];C:if((0|(A=e[f+132>>2]))!=6){if(!t){for(A-1>>>0<=4&&(e[55921]=A,e[55964]=e[110896+(A<<2)>>2]),A=e[f+88>>2],e[54741]=1,e[55922]=(0|A)/32,t=e[50758],A=e[50757];;){m:if((0|t)!=(0|(A=(0|(A=A+1|0))<=169?A:0)))if((0|(E=e[216192+(A<<4)>>2]))!=1){if(E-5>>>0>1)continue}else{if(e[54741]=0,A=e[8+(216192+(A<<4)|0)>>2],!(M[i+4>>1]!=M[A+4>>1]|M[A+6>>1]!=M[i+6>>1]|M[A+8>>1]!=M[i+8>>1]|M[A+10>>1]!=M[i+10>>1])&&M[A+12>>1]==M[i+12>>1])break m;e[54741]=2}break}for((M[o+4>>1]!=M[113564]|M[o+6>>1]!=M[113565]|M[o+8>>1]!=M[113566]|M[o+10>>1]!=M[113567]||M[o+12>>1]!=M[113568])&&(ji(),e[55974]=0,e[55975]=0,e[55972]=0,e[55973]=0,e[55988]=0,e[55989]=0,e[55990]=0,e[55991]=0,e[56004]=0,e[56005]=0,e[56006]=0,e[56007]=0,e[56020]=0,e[56021]=0,e[56022]=0,e[56023]=0,e[56036]=0,e[56037]=0,e[56038]=0,e[56039]=0,e[56052]=0,e[56053]=0,e[56054]=0,e[56055]=0,e[56068]=0,e[56069]=0,e[56070]=0,e[56071]=0,e[56086]=0,e[56087]=0,e[56084]=0,e[56085]=0,e[56102]=0,e[56103]=0,e[56100]=0,e[56101]=0,e[56118]=0,e[56119]=0,e[56116]=0,e[56117]=0,e[56134]=0,e[56135]=0,e[56132]=0,e[56133]=0,e[56150]=0,e[56151]=0,e[56148]=0,e[56149]=0,e[56166]=0,e[56167]=0,e[56164]=0,e[56165]=0,e[56182]=0,e[56183]=0,e[56180]=0,e[56181]=0,e[56198]=0,e[56199]=0,e[56196]=0,e[56197]=0,e[56214]=0,e[56215]=0,e[56212]=0,e[56213]=0,e[56230]=0,e[56231]=0,e[56228]=0,e[56229]=0),A=M[i+4>>1]|M[i+6>>1]<<16,t=M[i>>1]|M[i+2>>1]<<16,b[113562]=t,b[113563]=t>>>16,b[113564]=A,b[113565]=A>>>16,A=M[i+60>>1]|M[i+62>>1]<<16,t=M[i+56>>1]|M[i+58>>1]<<16,b[113590]=t,b[113591]=t>>>16,b[113592]=A,b[113593]=A>>>16,A=M[i+52>>1]|M[i+54>>1]<<16,t=M[i+48>>1]|M[i+50>>1]<<16,b[113586]=t,b[113587]=t>>>16,b[113588]=A,b[113589]=A>>>16,A=M[i+44>>1]|M[i+46>>1]<<16,t=M[i+40>>1]|M[i+42>>1]<<16,b[113582]=t,b[113583]=t>>>16,b[113584]=A,b[113585]=A>>>16,A=M[i+36>>1]|M[i+38>>1]<<16,t=M[i+32>>1]|M[i+34>>1]<<16,b[113578]=t,b[113579]=t>>>16,b[113580]=A,b[113581]=A>>>16,A=M[i+28>>1]|M[i+30>>1]<<16,t=M[i+24>>1]|M[i+26>>1]<<16,b[113574]=t,b[113575]=t>>>16,b[113576]=A,b[113577]=A>>>16,A=M[i+20>>1]|M[i+22>>1]<<16,t=M[i+16>>1]|M[i+18>>1]<<16,b[113570]=t,b[113571]=t>>>16,b[113572]=A,b[113573]=A>>>16,A=M[i+12>>1]|M[i+14>>1]<<16,t=M[i+8>>1]|M[i+10>>1]<<16,b[113566]=t,b[113567]=t>>>16,b[113568]=A,b[113569]=A>>>16,I=+(0|c),(L=1&b[o>>1])?(A=l[o+39|0],e[56680]=A,D[28364]=A>>>0,D[28354]=+(l[i+39|0]-A<<6)/I,A=l[o+40|0],D[28366]=A>>>0,D[28356]=+(l[i+40|0]-A<<6)/I,A=l[o+41|0],e[56682]=A,D[28368]=A>>>0,D[28358]=+(l[i+41|0]-A<<6)/I,t=l[o+42|0],e[56684]=t,D[28370]=t>>>0,A=l[o+43|0],a=+(l[i+43|0]-A<<6)/I,p=+(l[i+42|0]-t<<6)/I,m=+(A>>>0)):(e[56728]=0,e[56729]=0,A=0,e[56680]=0,e[56708]=0,e[56709]=0,e[56732]=0,e[56733]=0,e[56712]=0,e[56713]=0,e[56682]=0,e[56736]=0,e[56737]=0,e[56716]=0,e[56717]=0,e[56684]=0,e[56740]=0,e[56741]=0,m=0),e[56688]=A,D[28360]=p,D[28372]=m,D[28362]=a,e[56692]=0,e[56748]=0,e[56749]=0,e[56694]=0,e[56752]=0,e[56753]=0,e[56696]=0,e[56756]=0,e[56757]=0,e[56700]=0,e[56760]=0,e[56761]=0,e[56704]=0,e[56764]=0,e[56765]=0,e[50764]=c,A=1;T=b[164+(E=(c=A<<1)+f|0)>>1],t=(Qe=z(A,80))+222896|0,p=+b[E+218>>1],a=.00390625*+(0|z(T,b[2+(o+c|0)>>1]))+p,D[t+16>>3]=a,Z=Ne(a)<2147483648?~~a:-2147483648,e[t>>2]=Z,D[t+48>>3]=64*(.00390625*+(0|z(T,b[2+(i+c|0)>>1]))+p-a)/I,A>>>0<=3&&(t=Qe+222896|0,a=.00390625*+b[E+200>>1]*+(l[35+(A+o|0)|0]<<1),D[t+24>>3]=a,c=Ne(a)<2147483648?~~a:-2147483648,e[t+4>>2]=c,D[t+56>>3]=64*(+(l[35+(A+i|0)|0]<<1)-a)/I),(0|(A=A+1|0))!=6;);if(a=+((A=l[o+40|0])<<1),D[27864]=a,t=e[56618],A||(a=+(0|t),D[27864]=a),A=Ne(a)<2147483648?~~a:-2147483648,e[55724]=A,c=l[i+40|0],e[55730]=0,e[55731]=1079394304,e[55738]=0,e[55739]=0,e[55725]=89,A=1,D[27868]=64*(+(0|(c?c<<1:t))-a)/I,L)for(;t=z(A,80)+222896|0,f=l[56+(c=A+o|0)|0]<<2,e[t+12>>2]=f,a=+(0|f),D[t+40>>3]=a,f=A+i|0,D[t+72>>3]=64*(+(l[f+56|0]<<2)-a)/I,c=l[c+49|0],e[t+8>>2]=c,a=+(c>>>0),D[t+32>>3]=a,D[t- -64>>3]=64*(+l[f+49|0]-a)/I,(0|(A=A+1|0))!=7;);e[56606]=0}for(;;){if((0|(Z=e[50764]))>(0|(i=e[56606]))){for(A=e[50767],e[56609]=e[55724],e[56619]=e[55725],e[56610]=e[55744],e[56611]=e[55764],e[56612]=e[55784],e[56613]=e[55804],L=z(A,10),e[56607]=(0|L)/4096,e[56620]=e[55745],e[56621]=e[55765],e[56622]=e[55785],e[56614]=e[55824],e[56630]=e[55746],e[56631]=e[55766],e[56632]=e[55786],e[56633]=e[55806],e[56634]=e[55826],e[56635]=e[55846],Qe=e[56680],e[56608]=Qe,o=e[56694],e[56656]=o,c=e[56696],e[56653]=c,f=e[56700],e[56655]=f,E=e[56684],e[56649]=E,e[56651]=e[56704],e[56654]=e[56688],e[56652]=e[56682],e[56650]=e[56692],A=0;t=z(A,80)+222896|0,a=D[t+48>>3]+D[t+16>>3],D[t+16>>3]=a,I=D[t+56>>3]+D[t+24>>3],D[t+24>>3]=I,p=D[t+72>>3]+D[t+40>>3],D[t+40>>3]=p,m=D[t- -64>>3]+D[t+32>>3],D[t+32>>3]=m,T=Ne(a)<2147483648?~~a:-2147483648,e[t>>2]=T,T=Ne(I)<2147483648?~~I:-2147483648,e[t+4>>2]=T,T=Ne(p)<2147483648?~~p:-2147483648,e[t+12>>2]=T,T=Ne(m)<2147483648?~~m:-2147483648,e[t+8>>2]=T,(0|(A=A+1|0))!=9;);for(a=D[28354]+D[28364],D[28364]=a,D[28366]=D[28356]+D[28366],I=D[28358]+D[28368],D[28368]=I,p=D[28360]+D[28370],D[28370]=p,m=D[28362]+D[28372],D[28372]=m,A=Ne(a)<2147483648?~~a:-2147483648,e[56680]=A,A=Ne(I)<2147483648?~~I:-2147483648,e[56682]=A,A=Ne(p)<2147483648?~~p:-2147483648,e[56684]=A,A=Ne(m)<2147483648?~~m:-2147483648,e[56688]=A,a=D[28374]+0,D[28374]=a,A=Ne(a)<2147483648?~~a:-2147483648,e[56692]=A,a=D[28376]+0,D[28376]=a,A=Ne(a)<2147483648?~~a:-2147483648,e[56694]=A,a=D[28378]+0,D[28378]=a,A=Ne(a)<2147483648?~~a:-2147483648,e[56696]=A,a=D[28380]+0,D[28380]=a,A=Ne(a)<2147483648?~~a:-2147483648,e[56700]=A,a=D[28382]+0,D[28382]=a,A=Ne(a)<2147483648?~~a:-2147483648,e[56704]=A,e[56659]=e[55724],e[56669]=e[55725],e[56660]=e[55744],e[56670]=e[55745],e[56661]=e[55764],e[56671]=e[55765],e[56662]=e[55784],e[56672]=e[55785],e[56663]=e[55804],e[56664]=e[55824],e[56665]=e[55844],A=e[50768]+e[50769]|0,e[50768]=A,A>>=8,e[50767]=e[50770]+(z(e[50771],l[e[50766]+((0|A)>=127?127:A)|0])>>8),A=Z-i|0,e[55923]=(0|A)>=64?64:A,e[55961]=(0|L)/40960,A=Qe-7|0,e[56658]=(0|A)>0?A:0,D[27974]=E>>>0<=87?.001*+b[111136+(E<<1)>>1]*.05:0,D[27975]=c>>>0<=87?.001*+b[111136+(c<<1)>>1]*.25:0,D[27973]=o>>>0<=87?.001*+b[111136+(o<<1)>>1]:0,D[27971]=f>>>0<=87?.001*+b[111136+(f<<1)>>1]*.05:0,a=(A=e[56629])>>>0<=87?.001*+b[111136+(A<<1)>>1]*.6:0,D[r>>3]=a,a=(A=e[56630])>>>0<=87?.001*+b[111136+(A<<1)>>1]*.4:0,D[r+8>>3]=a,a=(A=e[56631])>>>0<=87?.001*+b[111136+(A<<1)>>1]*.15:0,D[r+16>>3]=a,a=(A=e[56632])>>>0<=87?.001*+b[111136+(A<<1)>>1]*.06:0,D[r+24>>3]=a,a=(A=e[56633])>>>0<=87?.001*+b[111136+(A<<1)>>1]*.04:0,D[r+32>>3]=a,a=(A=e[56634])>>>0<=87?.001*+b[111136+(A<<1)>>1]*.022:0,D[r+40>>3]=a,a=(A=e[56635])>>>0<=87?.001*+b[111136+(A<<1)>>1]*.03:0,D[r+48>>3]=a,a=(A=(0|(A=e[56657]-3|0))<=0?57:A)>>>0<=87?.001*+b[111136+(A<<1)>>1]:0,D[27977]=a/+e[55964],I=D[27968],p=D[27967],A=1;t=223664+(i=A<<6)|0,m=(a=Pt(p*+e[48+(c=226428+(o=A<<2)|0)>>2]))*-a,D[t+216>>3]=m,a*=Ga(I*+e[c+8>>2]),a+=a,D[t+208>>3]=a,he=1-a-m,D[t+200>>3]=he,A>>>0<=5&&(c=e[208+(o=o+226428|0)>>2],t=i+223664|0,ce=(ee=Pt(p*+e[o+248>>2]))*Ga(I*+(0|c)),ce+=ce,D[t+1488>>3]=ce,ee*=-ee,D[t+1496>>3]=ee,ve=1-ce-ee,D[t+1480>>3]=ve,D[t+256>>3]=.015625*(ee-m),D[t+248>>3]=.015625*(ce-a),D[t+240>>3]=.015625*(ve-he)),(0|(A=A+1|0))!=10;);for(ee=(a=Pt(p*+e[56619]))*-a,D[27985]=ee,ce=(a*=Ga(I*+(0-e[56609]|0)))+a,D[27984]=ce,a=1-ce-ee,D[27983]=a,a!=0&&(a=1/a,D[27983]=a,ee*=m=-a,D[27985]=ee,ce*=m,D[27984]=ce),he=(m=Pt(p*+e[56669]))*-m,D[28145]=he,ve=(m*=Ga(I*+(0-e[56659]|0)))+m,D[28144]=ve,m=1-ve-he,D[28143]=m,m!=0&&(m=1/m,D[28143]=m,he*=rA=-m,D[28145]=he,ve*=rA,D[28144]=ve),D[27990]=.015625*(he-ee),D[27989]=.015625*(ve-ce),D[27988]=.015625*(m-a),A=0;o=e[8+(i=226428+(A<<2)|0)>>2],t=223664+(A<<6)|0,m=(a=Pt(p*+e[i+128>>2]))*Ga(I*+(0|o)),m+=m,D[t+848>>3]=m,a*=-a,D[t+856>>3]=a,D[t+840>>3]=D[r+(A<<3)>>3]*(1-m-a),(0|(A=A+1|0))!=7;);if(p=(a=Pt(p*+(e[55918]/2|0)))*-a,D[28137]=p,a*=Ga(0*I),a+=a,D[28136]=a,D[28135]=1-a-p,A=1,(0|ZA())!=1)continue;break C}break}A=1,e[54741]>0&&(e[54741]=0,e[55963]=64,e[56606]=i+-64,e[55923]=64,(0|ZA())==1)||(A=0)}else{if(J=E=J-752|0,!t){jA(A=E+376|0,0,376),Bs(f,o,A),Bs(f,i,A=jA(E,0,376)),t=e[50768]+z(e[50769],c>>>6|0)|0,e[50768]=t,t>>=8,t=e[50770]+(z(e[50771],l[e[50766]+((0|t)>=127?127:t)|0])>>8)|0,e[50767]=t,D[A+368>>3]=(0|t)/4096|0,e[50773]&&(D[A+736>>3]=D[A+736>>3]/5,D[A+360>>3]=D[A+360>>3]/5),t=e[e[56797]+4>>2],ct[e[e[t>>2]>>2]](t,A+376|0,110,110,-1,0),i=c-110|0,f=e[50758],t=e[50757];m:{for(;;){if((0|f)!=(0|(t=(t+1|0)%170|0))&&!((T=e[216192+(t<<4)>>2])-5>>>0<2)){if(o=1,(0|T)!=1)continue;break m}break}i=c-220|0,o=0}(0|i)>0&&(t=e[e[56797]+4>>2],ct[e[e[t>>2]>>2]](t,A,i,i||1,-1,0)),o||(e[A+352>>2]=0,e[A+356>>2]=0,D[A>>3]=D[A+368>>3],t=e[e[56797]+4>>2],ct[e[e[t>>2]>>2]](t,A,55,55,-1,0),e[A+360>>2]=0,e[A+364>>2]=0,t=e[e[56797]+4>>2],ct[e[e[t>>2]>>2]](t,A,55,55,-1,0))}A=e[e[56797]+8>>2],t=e[51290],c=0|ct[e[e[A>>2]>>2]](A,e[54046]-t>>>1|0,t),f=e[51290];m:if(c&&!((0|(t=e[50776]))>=(0|(Qe=e[50773]))))for(We=e[50777],cA=(0|z(We,3))/-4|0,a=.0009765625*+e[50780],T=e[50772],i=e[50778],MA=e[50775],Z=e[50774],A=0;;){if(L=l[T+(o=t+i|0)|0],Z?L=z(Z,L<<24>>24):(t=t+1|0,e[50776]=t,L|=g[T+(o=t+i|0)|0]<<8),L=Ne(I=a*+(0|L))<2147483648?~~I:-2147483648,b[(Ke=f+(A<<1)|0)>>1]=M[Ke>>1]+((0|z(L,MA))/40|0),(0|o)>=(0|We)&&(i=i+cA|0,e[50778]=i),t=t+1|0,e[50776]=t,(0|t)>=(0|Qe))break m;if(!(c>>>0>(A=A+1|0)>>>0))break}A=f+(c<<1)|0,e[51290]=A,J=E+752|0,A=ue[54046]<=A>>>0}if(J=r- -64|0,A)break i;break c}e[50781]=A||100;break c}Ss(A,e[r+8>>2]);break c}A=e[r+8>>2],e[50759]=wt(203816,A,1344),e[50801]=e[A+108>>2]?105792:106064,A=(0|z(e[A+120>>2],26))/100|0,e[33038]=A,(0|(s=e[50754]))<=11e3&&(g[203300]=1,e[33038]=A<<1),e[54728]=e[50982],A=e[50979],t=e[50978],jA(205184,0,11e3),e[51293]=0,A=(t=(o=(0|(i=e[50789]))>0)?130:(0|t)>=5499?5499:t)?o?i:(0|A)>=100?100:A:0,e[50755]=A,t=(0|z(t,s))/1e3|0,e[51292]=t,e[54729]=(0|A)>20?t<<1:A?t:0,e[33037]=(0|z(500-A|0,(0|z(l[e[50797]+105596|0],(0|z(e[50787],55))/100|0))/16|0))/500,A=256,(0|(t=(0|(t=e[50785]))>=101?101:t))>=51&&(A=256+(((z(t,25)-1250&65535)>>>0)/50|0)|0),b[101990]=(0|z(b[102026],A))/256,b[101991]=(0|z(b[102027],A))/256,b[101992]=(0|z(b[102028],A))/256,b[101993]=(0|z(b[102029],A))/256,b[101994]=(0|z(b[102030],A))/256,b[101995]=(0|z(b[102031],A))/256,A=e[50790],b[101999]=(0|z(b[102035],z(A,-6)+256|0))/256,b[102e3]=(0|z(b[102036],z(A,-3)+256|0))/256,ti(8,0,e[50986],0,e[51290]),xt(e[r+8>>2]);break c}if(!e[50759])break c;t=e[r+12>>2],r=e[r+8>>2],e[55911]=0,e[55915]=A?2097152/(0|A)|0:0,e[55910]=r,A=(0|z(t,e[33037]))/16|0,e[50779]=A,e[50780]=(0|z(z(A,e[50985]),15))/100;break c}ti(t>>8,A,e[r+8>>2],e[r+12>>2],o)}s=0,A=e[50757]+1|0,e[50757]=(0|A)<=169?A:0}if(g[218920]=s,!((o=e[51290])>>>0<ue[54046]))break}i=e[34391],r=(e[51290]-i|0)/2|0,e[34439]=r+e[34439],t=e[34436],A=e[34388]+z(t,36)|0,e[A+4>>2]=0,e[A>>2]=0,e[A+24>>2]=e[34438];s:if(2&(A=e[32538])){if(o=e[34388],(-2&A)==2&&(!(r=t?o:0)|e[r>>2]!=8||(0|(r=e[r+28>>2]))!=e[34389]&&(e[34389]=r)),s=1,(0|t)<2)break s;for(;;){r=t?o+z(s,36)|0:0;i:{c:switch(0|A){case 2:case 3:if(!r|e[r>>2]!=8||(0|(r=e[r+28>>2]))==e[34389])break i;e[34389]=r;break i;case 0:break c;default:break i}(A=e[34440])?(ct[0|A](i,0,r),t=e[34436],A=e[32538]):A=0}if(!((0|(s=s+1|0))<(0|t)))break}}else if((A=e[34440])&&0|ct[0|A](i,r,e[34388]))break r;if(!oA(1)&&!(170-((0|(A=e[50757]-e[50758]|0))<=0?A+170|0:A)|0||(A=e[34388],e[A>>2]=0,e[A+4>>2]=e[34437],e[A+24>>2]=e[34438],me(1))))break}if(t=0,2&l[130152]||!(A=e[34440])||!(0|ct[0|A](0,0,e[34388])))break A}me(2),t=268439295}if((0|t)<=268437502){if(!t|(0|t)==268436479|(0|t)!=268437247)break e;return}}}function oA(A){var t,r=0,a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0;J=t=J-720|0,r=e[47198],A||(e[36443]=0,e[36442]=1,e[36444]=0,e[36440]=0,e[36441]=0,A=e[50758],e[36454]=A,e[36427]=-1,e[36424]=-1,e[36446]=0,e[36447]=0,e[36439]=-1,e[36426]=0,e[36455]=A,e[36448]=0,e[36449]=0,e[36450]=0,e[36451]=0,e[36452]=0,e[36453]=0,Oa(),e[36427]=-1,A=216192+(e[50758]<<4)|0,e[A>>2]=5,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,e[36426]=0,e[36438]&&(e[36438]=0,A=216192+(e[50758]<<4)|0,e[A>>2]=14,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0));e:{A:if(!((0|(A=e[36442]))>997|e[36423]<=(0|A)))for(L=r>>>1&1,Z=t+48|4,ee=t+348|0,ce=t+652|0,he=e[32322],I=e[32320],ve=t+60|0,Qe=t+648|0,We=t+56|0,Ke=t+620|0,E=t- -64|0;;){if(a=145840+(A<<5)|0,!(A=e[50756])|!e[A>>2]||(e[t+12>>2]=0,rs(A=t+16|0,e[a+8>>2],a,0,t+12|0),r=Xi(A),s=l[a+17|0],A=216192+(e[50758]<<4)|0,e[A>>2]=16,e[A+8>>2]=s,e[A+4>>2]=r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0),A=1,(0|(s=(0|(r=e[50757]-e[50758]|0))<=0?r+170|0:r))<=(0|((r=l[a+17|0])?(0|r)==2?25:15:10)))break e;if(m=e[36442],2&l[0|a])for(p=2047&M[a+4>>1];;){if(s=e[36443],c=127&(r=e[198304+(s<<2)>>2])){A=r>>>8|0,e[36443]=s+1;r:{s:{switch((31&r)-2|0){case 0:Ss(96&r|8,A),Os(2);break r;case 5:if((0|A)>=e[34064]||!e[4+(c=136272+(s=A<<4)|0)>>2])break r;dr(10,0),A=216192+(e[50758]<<4)|0,e[A>>2]=6,c=e[c+4>>2],e[A+8>>2]=e[8+(s+136272|0)>>2]+44,e[A+12>>2]=5376,e[A+4>>2]=c;break s;case 8:if((0|((0|(s=e[50757]-e[50758]|0))<=0?s+170:s))<6)break r;c=e[47353],s=216192+(e[50758]<<4)|0,e[s>>2]=778,e[s+8>>2]=A,e[s+4>>2]=c+p&16777215;break s;case 9:if((0|((0|(s=e[50757]-e[50758]|0))<=0?s+170:s))<6)break r;c=e[33284],s=216192+(e[50758]<<4)|0,e[s>>2]=1034,e[s+8>>2]=A,e[s+4>>2]=c+1&16777215;break s}dr(10,0),s=216192+(e[50758]<<4)|0,e[s>>2]=12,e[s+8>>2]=A,e[s+4>>2]=c}A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0}if(!(128&r))continue}break}(A=l[a+20|0])&&(1&g[e[47192]+48|0]&l[a+17|0]==2|1&g[e[a+8>>2]+7|0]||(e[36426]=0),r=e[47353]+(2047&M[a+4>>1])|0,e[36445]=r,4&A&&((0|((0|(A=e[50757]-e[50758]|0))<=0?A+170:A))<6||(s=e[47568],A=216192+(e[50758]<<4)|0,e[A>>2]=522,e[A+8>>2]=s,e[A+4>>2]=16777215&r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0)),1&g[a+20|0]&&(r=M[a+4>>1],s=e[36444],e[36444]=s+1,(0|((0|(A=e[50757]-e[50758]|0))<=0?A+170:A))<6||(p=e[36445],c=e[47355],A=216192+(e[50758]<<4)|0,e[A>>2]=266,e[A+8>>2]=s+c,e[A+4>>2]=16777215&p|(63488&r)<<13,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0))),(0|(A=e[36441]))>0&&(r=216192+(e[36425]<<4)|0,e[r+4>>2]||(e[r+4>>2]=A),e[36441]=0),A=m+1<<5,r=m-1<<5,!(s=l[a+18|0])|2&l[e[a+8>>2]+7|0]||dr(s,1),c=A+145840|0,i=r+145840|0,p=1;r:{s:{if(e[47198]&&(f=e[a+8>>2],l[f+10|0]!=15)){i:if(l[a+17|0]==2)switch(l[i+17|0]-3|0){case 0:case 5:break s;default:break i}p=0,rs(t+704|0,f,a,L,0),(0|((0|(A=e[50757]-e[50758]|0))<=0?A+170:A))<6||(r=e[36445],A=216192+(e[50758]<<4)|0,e[A>>2]=1802,e[A+4>>2]=16777215&r,r=e[t+708>>2],e[A+8>>2]=e[t+704>>2],e[A+12>>2]=r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0)}i:switch(l[a+17|0]){case 0:dr(e[a+12>>2],0),g[a+23|0]=l[e[a+8>>2]+14|0];break r;case 4:r=e[a+8>>2],(0|(A=l[c+17|0]))==2|!l[c+20|0]&(0|A)==3||(b[a>>1]=8192|M[a>>1]),2&l[r+7|0]&&(e[t+88>>2]=0,e[t+92>>2]=0,e[t+80>>2]=0,e[t+84>>2]=0,e[t+72>>2]=0,e[t+76>>2]=0,e[E>>2]=0,e[E+4>>2]=0,e[t+56>>2]=0,e[t+60>>2]=0,e[t+48>>2]=0,e[t+52>>2]=0,tr(0,1,a,t+552|0,145784),e[t+56>>2]=e[t+620>>2],e[E>>2]=e[t+640>>2],e[36424]<0&&(s=l[c+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[12+(A=216192+(A<<4)|0)>>2]=s,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,p=l[c+22|0],s=l[c+21|0],c=e[129280+(l[a+16|0]<<2)>>2],(0|(A=e[36424]))<0|(0|(f=e[36440]))<=0||e[4+(A=216192+(A<<4)|0)>>2]||(e[A+4>>2]=f),A=e[50758],e[36424]=A,e[36440]=0,e[(A=216192+(A<<4)|0)>>2]=9,e[A+4>>2]=0,p|=s<<16,s=(0|s)==255,e[A+12>>2]=s?3604556:p,e[A+8>>2]=s?I:c,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0),QA(r,0,t+48|0,a,0)),tr(0,0,a,t+552|0,145784),e[t+552>>2]=4|e[t+552>>2],(0|(A=e[36440]))<=0||(0|(r=e[36424]))<0||(e[4+(r=216192+(r<<4)|0)>>2]||(e[r+4>>2]=A),e[36440]=0),e[36426]=0,e[36439]=-1,e[36455]=e[50758],Oa(),e[36427]=-1,e[36422]=0,(A=e[t+624>>2])&&(s=A,A=e[t+644>>2],vs(s,2,e[t+596>>2]<<1,e[t+552>>2],0,A?(A<<5)/100|0:32)),e[36426]=0;break r;case 6:tr(0,0,a,t+552|0,145784),8&l[0|a]&&(A=e[a+12>>2],(0|(r=e[36440]))<=0||(0|(s=e[36424]))<0||(e[4+(s=216192+(s<<4)|0)>>2]||(e[s+4>>2]=r),e[36440]=0),e[36426]=0,e[36439]=-1,e[36455]=e[50758],Oa(),e[36427]=-1,e[36422]=0,(r=e[t+624>>2])&&(s=A,A=e[t+644>>2],vs(r,2,e[t+596>>2]<<1,e[t+552>>2],s,A?(A<<5)/100|0:32))),A=e[a+12>>2],(0|(r=e[36440]))<=0||(0|(s=e[36424]))<0||(e[4+(s=216192+(s<<4)|0)>>2]||(e[s+4>>2]=r),e[36440]=0),e[36426]=0,e[36439]=-1,e[36455]=e[50758],Oa(),e[36427]=-1,e[36422]=0,(r=e[t+624>>2])&&(s=A,A=e[t+644>>2],vs(r,2,e[t+596>>2]<<1,e[t+552>>2],s,A?(A<<5)/100|0:32)),e[36426]=0;break r;case 5:s=e[a+8>>2],e[Z+40>>2]=0,e[(A=Z)+32>>2]=0,e[A+36>>2]=0,e[A+24>>2]=0,e[A+28>>2]=0,e[A+16>>2]=0,e[A+20>>2]=0,e[A+8>>2]=0,e[A+12>>2]=0,e[A>>2]=0,e[A+4>>2]=0,e[t+48>>2]=4;c:{o:{u:{l:switch(l[c+17|0]-2|0){case 0:r=l[a+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[12+(A=216192+(A<<4)|0)>>2]=r,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,r=l[a+22|0],f=l[a+21|0],p=e[129280+(l[a+16|0]<<2)>>2],(0|(A=e[36424]))<0|(0|(o=e[36440]))<=0||e[4+(A=216192+(A<<4)|0)>>2]||(e[A+4>>2]=o),A=(o=!(255&~f))?I:p,p=1;break o;case 1:break l;default:break u}if(!l[c+20|0]){r=l[c+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[12+(A=216192+(A<<4)|0)>>2]=r,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,r=l[c+22|0],f=l[c+21|0],p=e[129280+(l[c+16|0]<<2)>>2],(0|(A=e[36424]))<0|(0|(o=e[36440]))<=0||e[4+(A=216192+(A<<4)|0)>>2]||(e[A+4>>2]=o),A=(o=!(255&~f))?I:p,p=1;break o}}if(p=0,e[36424]>=0)break c;r=l[c+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[12+(A=216192+(A<<4)|0)>>2]=r,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,r=l[a+22|0],f=l[a+21|0],p=e[129280+(l[a+16|0]<<2)>>2],(0|(A=e[36424]))<0|(0|(o=e[36440]))<=0||e[4+(A=216192+(A<<4)|0)>>2]||(e[A+4>>2]=o),A=(o=!(255&~f))?I:p,p=0}T=e[50758],e[36424]=T,e[36440]=0,e[(T=216192+(T<<4)|0)>>2]=9,e[T+4>>2]=0,e[T+12>>2]=o?3604556:255&r|(255&f)<<16,e[T+8>>2]=A,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0}c:if(!(2&l[s+7|0])&l[i+17|0]!=2)8&l[0|a]&&dr(50,0);else{if(tr(0,1,a,t+552|0,145784),e[t+56>>2]=e[t+620>>2],e[t+64>>2]=e[t+640>>2],QA(s,0,t+48|0,a,0),!(8&l[0|a]))break c;dr(25,1),QA(s,0,t+48|0,a,0)}c:if(p){if(e[36455]!=e[36454])break c;e[36455]=e[50758]}else b[a>>1]=8192|M[a>>1];if(tr(0,0,a,t+552|0,145784),e[t+56>>2]=e[t+620>>2],e[t+64>>2]=e[t+640>>2],e[t+76>>2]=e[t+636>>2],e[t+80>>2]=e[t+656>>2],QA(s,0,t+48|0,a,0),l[a+20|0]|l[84+(145840+(m<<5)|0)|0]||((0|(A=l[c+17|0]))==7&&(dr(20,0),A=l[c+17|0]),(255&A)!=6))break r;dr(12,0);break r;case 7:c:{o:{u:{l:{n:switch((A=l[c+17|0])-2|0){case 1:break l;case 0:break n;default:break u}r=l[a+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[12+(A=216192+(A<<4)|0)>>2]=r,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,p=l[a+22|0],A=l[a+21|0],s=e[129280+(l[a+16|0]<<2)>>2],(0|(r=e[36424]))<0|(0|(f=e[36440]))<=0||e[4+(r=216192+(r<<4)|0)>>2]||(e[r+4>>2]=f),r=(f=!(255&~A))?I:s;break o}r=l[c+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[12+(A=216192+(A<<4)|0)>>2]=r,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,p=l[c+22|0],A=l[c+21|0],s=e[129280+(l[c+16|0]<<2)>>2],(0|(r=e[36424]))<0|(0|(f=e[36440]))<=0||e[4+(r=216192+(r<<4)|0)>>2]||(e[r+4>>2]=f),r=(f=!(255&~A))?I:s;break o}if(e[36424]>=0)break c;r=l[a+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[12+(A=216192+(A<<4)|0)>>2]=r,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,p=l[a+22|0],A=l[a+21|0],s=e[129280+(l[a+16|0]<<2)>>2],(0|(r=e[36424]))<0|(0|(f=e[36440]))<=0||e[4+(r=216192+(r<<4)|0)>>2]||(e[r+4>>2]=f),r=(f=!(255&~A))?I:s}s=e[50758],e[36424]=s,e[36440]=0,e[(s=216192+(s<<4)|0)>>2]=9,e[s+4>>2]=0,e[s+12>>2]=f?3604556:(255&A)<<16|p,e[s+8>>2]=r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,A=l[c+17|0]}c:{o:{u:switch((255&A)-2|0){case 1:if(l[c+20|0])break o;break;case 0:break u;default:break o}if(e[36455]!=e[36454])break c;e[36455]=e[50758];break c}b[a>>1]=8192|M[a>>1]}tr(0,0,a,t+552|0,145784),e[t+56>>2]=0,e[t+60>>2]=0,e[E>>2]=0,e[E+4>>2]=0,e[t+80>>2]=0,e[t+84>>2]=0,e[t+72>>2]=0,e[t+76>>2]=0,e[t+88>>2]=0,e[t+56>>2]=e[t+620>>2],e[E>>2]=e[t+640>>2],e[t+80>>2]=e[t+656>>2],e[t+48>>2]=0,e[t+52>>2]=0,e[t+76>>2]=e[t+636>>2],e[t+92>>2]=e[t+596>>2]<<1,8&l[0|a]&&QA(e[a+8>>2],0,t+48|0,a,0),QA(e[a+8>>2],0,t+48|0,a,0);break r;case 8:if(e[t+88>>2]=0,e[t+92>>2]=0,e[t+80>>2]=0,e[t+84>>2]=0,e[t+72>>2]=0,e[t+76>>2]=0,e[E>>2]=0,e[E+4>>2]=0,e[t+56>>2]=0,e[t+60>>2]=0,e[t+48>>2]=0,e[t+52>>2]=0,1&g[0|a]||(r=l[a+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[12+(A=216192+(A<<4)|0)>>2]=r,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,s=l[a+22|0],r=l[a+21|0],p=e[129280+(l[a+16|0]<<2)>>2],(0|(A=e[36424]))<0|(0|(f=e[36440]))<=0||e[4+(A=216192+(A<<4)|0)>>2]||(e[A+4>>2]=f),A=e[50758],e[36424]=A,e[36440]=0,e[(A=216192+(A<<4)|0)>>2]=9,e[A+4>>2]=0,s|=r<<16,r=(0|r)==255,e[A+12>>2]=r?3604556:s,e[A+8>>2]=r?I:p,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0),l[i+17|0]==8&&(e[36426]=0),tr(0,0,a,t+552|0,145784),e[t+56>>2]=e[t+620>>2],e[t+64>>2]=e[t+640>>2],e[t+92>>2]=e[t+596>>2]<<1,l[c+17|0]==2){e[36455]==e[36454]&&(e[36455]=e[50758]),QA(e[a+8>>2],0,t+48|0,a,0);break r}if(!(!(1&g[0|a])|l[i+17|0]!=2)){QA(e[a+8>>2],0,t+48|0,a,0);break r}e[36426]=0,QA(e[a+8>>2],0,t+48|0,a,0),e[36426]=0;break r;case 3:e[t+88>>2]=0,e[t+92>>2]=0,e[t+80>>2]=0,e[t+84>>2]=0,e[t+72>>2]=0,e[t+76>>2]=0,e[E>>2]=0,e[E+4>>2]=0,e[t+56>>2]=0,e[t+60>>2]=0,e[t+48>>2]=0,e[t+52>>2]=0,s=e[e[a+8>>2]+4>>2],1&g[0|a]||(r=l[a+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[12+(A=216192+(A<<4)|0)>>2]=r,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,p=l[a+22|0],r=l[a+21|0],f=e[129280+(l[a+16|0]<<2)>>2],(0|(A=e[36424]))<0|(0|(o=e[36440]))<=0||e[4+(A=216192+(A<<4)|0)>>2]||(e[A+4>>2]=o),A=e[50758],e[36424]=A,e[36440]=0,e[(A=216192+(A<<4)|0)>>2]=9,e[A+4>>2]=0,p|=r<<16,r=(0|r)==255,e[A+12>>2]=r?3604556:p,e[A+8>>2]=r?I:f,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0),l[i+17|0]==8&&(e[36426]=0),l[c+17|0]!=2|e[36455]!=e[36454]||(e[36455]=e[50758]),tr(0,0,a,t+552|0,145784),(0|(A=e[t+584>>2]-l[a+18|0]|0))>0&&dr(A,1),e[t+56>>2]=e[t+620>>2],e[t+64>>2]=e[t+640>>2],e[t+76>>2]=e[t+636>>2],e[t+80>>2]=e[t+656>>2],e[t+92>>2]=e[t+596>>2]<<1,QA(e[a+8>>2],0,t+48|0,a,s<<24>>31&5);break r;case 2:break i;default:break r}f=e[a+8>>2]}m=l[a+3|0],e[t+88>>2]=0,e[t+92>>2]=0,e[t+80>>2]=0,e[t+84>>2]=0,e[t+72>>2]=0,e[t+76>>2]=0,e[E>>2]=0,e[E+4>>2]=0,e[t+56>>2]=0,e[t+60>>2]=0,e[t+48>>2]=0,e[t+52>>2]=0,tr(0,0,a,t+552|0,145784),A=e[t+628>>2],e[t+56>>2]=A,e[t+92>>2]=e[t+596>>2]<<1;s:{if(!A||(o=0,s=Qe,r=ve,2&l[t+552|0])){if(l[i+17|0]?(o=0,tr(0,0,i,t+400|0,0),A=e[t+476>>2],e[t+56>>2]=A,!A|!(2&l[t+400|0])||(e[t+72>>2]=e[t+496>>2],o=1),r=e[t+512>>2],e[t+84>>2]=e[t+508>>2],e[t+88>>2]=r):o=0,A)break s;e[t+48>>2]=1,e[t+52>>2]=1,s=Ke,r=We}e[r>>2]=e[s>>2]}e[t+64>>2]=e[t+640>>2],r=l[a+16|0],A=0,(s=l[a+7|0])?(Hi(s,t+96|0),r=On(e[t+220>>2]),(0|(s=e[t+224>>2]))<=0||(A=On(s))):r=e[129280+(r<<2)>>2],e[36455]==e[36454]&&(e[36455]=e[50758]),s=(s=15&m)>>>0<2?1:s>>>0>6?3:2;s:{switch(l[i+17|0]-3|0){case 2:case 4:o=l[a+19|0],i=e[50758],e[36425]=i,e[36441]=0,e[12+(i=216192+(i<<4)|0)>>2]=o,e[i+8>>2]=A,e[i>>2]=8,e[i+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,o=l[a+22|0],i=l[a+21|0],(0|(A=e[36440]))<=0||(0|(m=e[36424]))<0||e[4+(m=216192+(m<<4)|0)>>2]||(e[m+4>>2]=A),A=e[50758],e[36424]=A,e[36440]=0,e[(A=216192+(A<<4)|0)>>2]=9,e[A+4>>2]=0,o|=i<<16,i=(0|i)==255,e[A+12>>2]=i?3604556:o,e[A+8>>2]=i?I:r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,QA(f,1,t+48|0,a,s);break s;case 0:case 5:o=l[a+19|0],i=e[50758],e[36425]=i,e[36441]=0,e[12+(i=216192+(i<<4)|0)>>2]=o,e[i+8>>2]=A,e[i>>2]=8,e[i+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,QA(f,1,t+48|0,a,s),o=l[a+22|0],i=l[a+21|0],(0|(A=e[36440]))<=0||(0|(m=e[36424]))<0||e[4+(m=216192+(m<<4)|0)>>2]||(e[m+4>>2]=A),A=e[50758],e[36424]=A,e[36440]=0,e[(A=216192+(A<<4)|0)>>2]=9,e[A+4>>2]=0,o|=i<<16,i=(0|i)==255,e[A+12>>2]=i?3604556:o,e[A+8>>2]=i?I:r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0;break s}o?(o=l[a+22|0],(0|(i=e[36440]))<=0||(0|(m=e[36424]))<0||e[4+(m=216192+(m<<4)|0)>>2]||(e[m+4>>2]=i),i=e[50758],e[36424]=i,e[36440]=0,e[(i=216192+(i<<4)|0)>>2]=9,e[i+4>>2]=0,e[i+12>>2]=(o|o<<16)-983040,e[i+8>>2]=he,i=e[50758]+1|0,e[50758]=(0|i)<=169?i:0,o=l[a+19|0],i=e[50758],e[36425]=i,e[36441]=0,e[(i=216192+(i<<4)|0)>>2]=8,e[i+4>>2]=0,e[i+12>>2]=o-1,e[i+8>>2]=A,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,QA(f,1,t+48|0,a,s),i=l[a+21|0],o=l[a+22|0],(0|(A=e[36440]))<=0||(0|(m=e[36424]))<0||e[4+(m=216192+(m<<4)|0)>>2]||(e[m+4>>2]=A),A=e[50758],e[36424]=A,e[36440]=0,e[(A=216192+(A<<4)|0)>>2]=9,e[A+4>>2]=0,o|=i<<16,i=(0|i)==255,e[A+12>>2]=i?3604556:o,e[A+8>>2]=i?I:r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0):(1&g[0|a]||(o=l[a+19|0],i=e[50758],e[36425]=i,e[36441]=0,e[12+(i=216192+(i<<4)|0)>>2]=o,e[i+8>>2]=A,e[i>>2]=8,e[i+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,o=l[a+22|0],i=l[a+21|0],(0|(A=e[36440]))<=0||(0|(m=e[36424]))<0||e[4+(m=216192+(m<<4)|0)>>2]||(e[m+4>>2]=A),A=e[50758],e[36424]=A,e[36440]=0,e[(A=216192+(A<<4)|0)>>2]=9,e[A+4>>2]=0,o|=i<<16,i=(0|i)==255,e[A+12>>2]=i?3604556:o,e[A+8>>2]=i?I:r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0),QA(f,1,t+48|0,a,s))}!e[47198]|1^p||(rs(t+704|0,e[a+8>>2],a,L,0),(0|((0|(A=e[50757]-e[50758]|0))<=0?A+170:A))<6||(r=e[36445],A=216192+(e[50758]<<4)|0,e[A>>2]=1802,e[A+4>>2]=16777215&r,r=e[t+708>>2],e[A+8>>2]=e[t+704>>2],e[A+12>>2]=r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0)),e[t+56>>2]=e[t+620>>2],e[t+84>>2]=0,e[t+88>>2]=0,e[t+64>>2]=e[t+640>>2],r=e[t+632>>2],e[t+68>>2]=r,A=ce;s:{if(!r&&(!l[c+17|0]||(e[t+72>>2]=0,tr(0,0,c,t+248|0,0),e[t+52>>2]=1,A=e[t+368>>2],e[t+84>>2]=e[t+364>>2],e[t+88>>2]=A,r=e[t+328>>2],e[t+68>>2]=r,A=ee,!r)))break s;e[t+72>>2]=e[A>>2]}QA(f,2,t+48|0,a,s)}if(A=e[36442]+1|0,e[36442]=A,(0|A)>997)break A;if(!(e[36423]>(0|A)))break}(0|(A=e[36440]))<=0||(0|(r=e[36424]))<0||(e[4+(r=216192+(r<<4)|0)>>2]||(e[r+4>>2]=A),e[36440]=0),e[36426]=0,e[36439]=-1,e[36455]=e[50758],Oa(),e[36427]=-1,A=0,e[36423]<=0||(r=e[47568],s=e[33284],(0|((0|(A=e[50757]-e[50758]|0))<=0?A+170:A))>=6&&(A=216192+(e[50758]<<4)|0,e[A>>2]=1290,e[A+8>>2]=r,e[A+4>>2]=16777215&s,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0),e[36423]=0,A=0)}return J=t+720|0,A}function BA(A,t,r,a){var s,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0,MA=0,zA=0,et=0,at=0,dt=0;J=s=J-48|0;e:{if(r>>>0<=2){for(MA=e[124732+(r<<=2)>>2],zA=e[r+124720>>2];(0|(r=e[t+4>>2]))==e[t+104>>2]?r=bA(t):(e[t+4>>2]=r+1,r=l[0|r]),(0|r)==32|r-9>>>0<5;);T=1;A:{r:switch(r-43|0){case 0:case 2:break r;default:break A}T=(0|r)==45?-1:1,(0|(r=e[t+4>>2]))==e[t+104>>2]?r=bA(t):(e[t+4>>2]=r+1,r=l[0|r])}A:{r:{for(;;){if(g[c+84056|0]==(32|r)){if(c>>>0>6||((0|(r=e[t+4>>2]))==e[t+104>>2]?r=bA(t):(e[t+4>>2]=r+1,r=l[0|r])),(0|(c=c+1|0))!=8)continue;break r}break}if((0|c)!=3){if((0|c)==8)break r;if(!a|c>>>0<4)break A;if((0|c)==8)break r}if((0|(r=e[t+116>>2]))>0|(0|r)>=0&&(e[t+4>>2]=e[t+4>>2]-1),!(!a|c>>>0<4))for(r=(0|r)<0;r||(e[t+4>>2]=e[t+4>>2]-1),(c=c-1|0)>>>0>3;);}J=I=J-16|0,ye(Re(Re(0|T)*Re(1/0))),(t=2147483647&(f=X(2)))-8388608>>>0<=2130706431?(r=t,r<<=25,a=1065353216+(t=t>>>7|0)|0):(r=f<<25,a=f>>>7|2147418112,t>>>0>=2139095040||(r=0,a=0,t&&(ea(I,r=t,0,0,0,(t=Ye(t))+81|0),p=e[I>>2],m=e[I+4>>2],r=e[I+8>>2],a=65536^e[I+12>>2]|16265-t<<16))),e[s>>2]=p,e[s+4>>2]=m,e[s+8>>2]=r,e[s+12>>2]=-2147483648&f|a,J=I+16|0,p=e[s+8>>2],m=e[s+12>>2],f=e[s>>2],E=e[s+4>>2];break e}A:{r:{s:if(!c){for(c=0;;){if(g[c+84473|0]!=(32|r))break s;if(c>>>0>1||((0|(r=e[t+4>>2]))==e[t+104>>2]?r=bA(t):(e[t+4>>2]=r+1,r=l[0|r])),(0|(c=c+1|0))==3)break}break r}switch(0|c){case 0:if((0|r)==48){if((0|(c=e[t+4>>2]))==e[t+104>>2]?c=bA(t):(e[t+4>>2]=c+1,c=l[0|c]),(-33&c)==88){J=o=J-432|0,(0|(r=e[t+4>>2]))==e[t+104>>2]?c=bA(t):(e[t+4>>2]=r+1,c=l[0|r]);s:{i:{for(;;){if((0|c)!=48){if((0|c)!=46)break s;if((0|(r=e[t+4>>2]))!=e[t+104>>2]){e[t+4>>2]=r+1,c=l[0|r];break i}break}(0|(r=e[t+4>>2]))!=e[t+104>>2]?(cA=1,e[t+4>>2]=r+1,c=l[0|r]):(cA=1,c=bA(t))}c=bA(t)}if(ee=1,(0|c)==48){for(;ce=(r=ce)-1|0,he=he-!r|0,(0|(r=e[t+4>>2]))==e[t+104>>2]?c=bA(t):(e[t+4>>2]=r+1,c=l[0|r]),(0|c)==48;);cA=1}}for(E=1073676288;;){s:{r=32|c;i:{if(!((et=c-48|0)>>>0<10)){if((0|c)!=46&r-97>>>0>=6)break s;if((0|c)==46){if(ee)break s;ee=1,ce=p,he=m;break i}}r=(0|c)>57?r-87|0:et,(0|m)<=0&p>>>0<=7|(0|m)<0?i=r+(i<<4)|0:!m&p>>>0<=28?(Pa(o+48|0,r),PA(o+32|0,Ke,rA,f,E,0,0,0,1073414144),Ke=e[o+32>>2],rA=e[o+36>>2],f=e[o+40>>2],E=e[o+44>>2],PA(o+16|0,e[o+48>>2],e[o+52>>2],e[o+56>>2],e[o+60>>2],Ke,rA,f,E),$r(o,e[o+16>>2],e[o+20>>2],e[o+24>>2],e[o+28>>2],I,Z,Qe,We),Qe=e[o+8>>2],We=e[o+12>>2],I=e[o>>2],Z=e[o+4>>2]):L|!r||(PA(o+80|0,Ke,rA,f,E,0,0,0,1073610752),$r(o- -64|0,e[o+80>>2],e[o+84>>2],e[o+88>>2],e[o+92>>2],I,Z,Qe,We),Qe=e[o+72>>2],We=e[o+76>>2],L=1,I=e[o+64>>2],Z=e[o+68>>2]),m=(p=p+1|0)?m:m+1|0,cA=1}(0|(r=e[t+4>>2]))!=e[t+104>>2]?(e[t+4>>2]=r+1,c=l[0|r]):c=bA(t);continue}break}s:if(cA){if((0|m)<=0&p>>>0<=7|(0|m)<0)for(f=p,E=m;i<<=4,(0|(f=f+1|0))!=8|(E=f?E:E+1|0););i:{c:{o:{if((-33&c)==80){if(f=Ci(t,a),E=r=$,f|(0|r)!=-2147483648)break i;if(a){if((0|(r=e[t+116>>2]))>0|(0|r)>=0)break o;break c}I=0,Z=0,Ja(t,0,0),r=0,t=0;break s}if(f=0,E=0,e[t+116>>2]<0)break i}e[t+4>>2]=e[t+4>>2]-1}f=0,E=0}if(i)if(r=f+((t=ee?ce:p)<<2)|0,t=(m=(ee?he:m)<<2|t>>>30)+E|0,(p=r-32|0)>>>0>0-MA>>>0&(0|(t=m=(r>>>0<f>>>0?t+1|0:t)-(r>>>0<32)|0))>=0|(0|t)>0)e[56798]=68,Pa(o+160|0,T),PA(o+144|0,e[o+160>>2],e[o+164>>2],e[o+168>>2],e[o+172>>2],-1,-1,-1,2147418111),PA(o+128|0,e[o+144>>2],e[o+148>>2],e[o+152>>2],e[o+156>>2],-1,-1,-1,2147418111),I=e[o+128>>2],Z=e[o+132>>2],r=e[o+140>>2],t=e[o+136>>2];else if((0|m)>=(0|(r=(t=MA-226|0)>>31))&t>>>0<=p>>>0|(0|r)<(0|m)){if((0|i)>=0)for(;$r(o+416|0,I,Z,Qe,We,0,0,0,-1073807360),$r(o+400|0,I,Z,Qe,We,(t=r=(0|(t=l0(I,Z,Qe,We,1073610752)))>=0)?e[o+416>>2]:I,t?e[o+420>>2]:Z,t?e[o+424>>2]:Qe,t?e[o+428>>2]:We),p=(t=p)-1|0,m=m-!t|0,Qe=e[o+408>>2],We=e[o+412>>2],I=e[o+400>>2],Z=e[o+404>>2],(0|(i=r|i<<1))>=0;);t=m-((MA>>31)+(p>>>0<MA>>>0)|0)|0,(0|(r=(r=32+(p-MA|0)|0)>>>0<zA>>>0&(0|(t=r>>>0<32?t+1|0:t))<=0|(0|t)<0?(0|r)>0?r:0:zA))>=113?(Pa(o+384|0,T),ce=e[o+392>>2],he=e[o+396>>2],Ke=e[o+384>>2],rA=e[o+388>>2],f=0,t=0):(Nr(o+352|0,Ws(1,144-r|0)),Pa(o+336|0,T),Ke=e[o+336>>2],rA=e[o+340>>2],ce=e[o+344>>2],he=e[o+348>>2],zn(o+368|0,e[o+352>>2],e[o+356>>2],e[o+360>>2],e[o+364>>2],Ke,rA,ce,he),ve=e[o+376>>2],at=e[o+380>>2],f=e[o+372>>2],t=e[o+368>>2]),si(o+320|0,(a=!(1&i)&!!(0|gA(I,Z,Qe,We,0,0,0,0))&(0|r)<32)+i|0),PA(o+304|0,Ke,rA,ce,he,e[o+320>>2],e[o+324>>2],e[o+328>>2],e[o+332>>2]),r=t,$r(o+272|0,e[o+304>>2],e[o+308>>2],e[o+312>>2],e[o+316>>2],t,f,ve,at),PA(o+288|0,Ke,rA,ce,he,(t=a)?0:I,t?0:Z,t?0:Qe,t?0:We),$r(o+256|0,e[o+288>>2],e[o+292>>2],e[o+296>>2],e[o+300>>2],e[o+272>>2],e[o+276>>2],e[o+280>>2],e[o+284>>2]),Wi(o+240|0,e[o+256>>2],e[o+260>>2],e[o+264>>2],e[o+268>>2],r,f,ve,at),gA(t=e[o+240>>2],a=e[o+244>>2],r=e[o+248>>2],f=e[o+252>>2],0,0,0,0)||(e[56798]=68),cr(o+224|0,t,a,r,f,p),I=e[o+224>>2],Z=e[o+228>>2],r=e[o+236>>2],t=e[o+232>>2]}else e[56798]=68,Pa(o+208|0,T),PA(o+192|0,e[o+208>>2],e[o+212>>2],e[o+216>>2],e[o+220>>2],0,0,0,65536),PA(o+176|0,e[o+192>>2],e[o+196>>2],e[o+200>>2],e[o+204>>2],0,0,0,65536),I=e[o+176>>2],Z=e[o+180>>2],r=e[o+188>>2],t=e[o+184>>2];else Nr(o+112|0,0*+(0|T)),I=e[o+112>>2],Z=e[o+116>>2],r=e[o+124>>2],t=e[o+120>>2]}else{i:{c:{if((0|(r=e[t+116>>2]))>0|(0|r)>=0){if(r=e[t+4>>2],e[t+4>>2]=r-1,!a)break c;if(e[t+4>>2]=r-2,!ee)break i;e[t+4>>2]=r-3;break i}if(a)break i}Ja(t,0,0)}Nr(o+96|0,0*+(0|T)),I=e[o+96>>2],Z=e[o+100>>2],r=e[o+108>>2],t=e[o+104>>2]}e[s+16>>2]=I,e[s+20>>2]=Z,e[s+24>>2]=t,e[s+28>>2]=r,J=o+432|0,p=e[s+24>>2],m=e[s+28>>2],f=e[s+16>>2],E=e[s+20>>2];break e}e[t+116>>2]<0||(e[t+4>>2]=e[t+4>>2]-1)}c=t,ve=T,o=a,t=0,T=0,J=i=J-8976|0,at=(et=0-MA|0)-zA|0;s:{i:{for(;;){if((0|r)!=48){if((0|r)!=46)break s;if((0|(r=e[c+4>>2]))!=e[c+104>>2]){e[c+4>>2]=r+1,r=l[0|r];break i}break}(0|(t=e[c+4>>2]))!=e[c+104>>2]?(e[c+4>>2]=t+1,r=l[0|t]):r=bA(c),t=1}r=bA(c)}if(L=1,(0|r)==48){for(;p=(t=p)-1|0,m=m-!t|0,(0|(t=e[c+4>>2]))==e[c+104>>2]?r=bA(c):(e[c+4>>2]=t+1,r=l[0|t]),(0|r)==48;);t=1}}e[i+784>>2]=0;s:{i:{c:{o:{u:{if((a=(0|r)==46)|(I=r-48|0)>>>0<=9)for(;;){l:{if(1&a){if(!L){p=f,m=E,L=1;break l}a=!t;break u}E=(f=f+1|0)?E:E+1|0,(0|T)<=2044?(cA=(0|r)==48?cA:f,t=(i+784|0)+(T<<2)|0,ee&&(I=(z(e[t>>2],10)+r|0)-48|0),e[t>>2]=I,t=1,ee=(r=(0|(a=ee+1|0))==9)?0:a,T=r+T|0):(0|r)!=48&&(e[i+8960>>2]=1|e[i+8960>>2],cA=18396)}if((0|(r=e[c+4>>2]))==e[c+104>>2]?r=bA(c):(e[c+4>>2]=r+1,r=l[0|r]),!((a=(0|r)==46)|(I=r-48|0)>>>0<10))break}if(p=L?p:f,m=L?m:E,!(!t|(-33&r)!=69)){if(I=Ci(c,o),Z=t=$,!(I|(0|t)!=-2147483648)){if(!o)break c;I=0,Z=0,e[c+116>>2]<0||(e[c+4>>2]=e[c+4>>2]-1)}m=m+Z|0,m=(p=p+I|0)>>>0<I>>>0?m+1|0:m;break i}if(a=!t,(0|r)<0)break o}e[c+116>>2]<0||(e[c+4>>2]=e[c+4>>2]-1)}if(!a)break i;e[56798]=28}f=0,E=0,Ja(c,0,0),r=0,t=0;break s}if(t=e[i+784>>2])if(f>>>0>9&(0|E)>=0|(0|E)>0|(0|f)!=(0|p)|(0|m)!=(0|E)|(t>>>zA|0?(0|zA)<=30:0))if(p>>>0>et>>>1>>>0&(0|m)>=0|(0|m)>0)e[56798]=68,Pa(i+96|0,ve),PA(i+80|0,e[i+96>>2],e[i+100>>2],e[i+104>>2],e[i+108>>2],-1,-1,-1,2147418111),PA(i- -64|0,e[i+80>>2],e[i+84>>2],e[i+88>>2],e[i+92>>2],-1,-1,-1,2147418111),f=e[i+64>>2],E=e[i+68>>2],r=e[i+76>>2],t=e[i+72>>2];else if((r=p>>>0<(t=MA-226|0)>>>0)&(0|m)<=(0|(t>>=31))|(0|t)>(0|m))e[56798]=68,Pa(i+144|0,ve),PA(i+128|0,e[i+144>>2],e[i+148>>2],e[i+152>>2],e[i+156>>2],0,0,0,65536),PA(i+112|0,e[i+128>>2],e[i+132>>2],e[i+136>>2],e[i+140>>2],0,0,0,65536),f=e[i+112>>2],E=e[i+116>>2],r=e[i+124>>2],t=e[i+120>>2];else{if(ee){if((0|ee)<=8){for(c=e[(t=(i+784|0)+(T<<2)|0)>>2];c=z(c,10),(0|(ee=ee+1|0))!=9;);e[t>>2]=c}T=T+1|0}if(L=p,!((0|cA)>(0|p)|(0|cA)>=9|(0|p)>17)){if((0|L)==9){Pa(i+192|0,ve),si(i+176|0,e[i+784>>2]),PA(i+160|0,e[i+192>>2],e[i+196>>2],e[i+200>>2],e[i+204>>2],e[i+176>>2],e[i+180>>2],e[i+184>>2],e[i+188>>2]),f=e[i+160>>2],E=e[i+164>>2],r=e[i+172>>2],t=e[i+168>>2];break s}if((0|L)<=8){Pa(i+272|0,ve),si(i+256|0,e[i+784>>2]),PA(i+240|0,e[i+272>>2],e[i+276>>2],e[i+280>>2],e[i+284>>2],e[i+256>>2],e[i+260>>2],e[i+264>>2],e[i+268>>2]),Pa(i+224|0,e[124720+(0-L<<2)>>2]),Qr(i+208|0,e[i+240>>2],e[i+244>>2],e[i+248>>2],e[i+252>>2],e[i+224>>2],e[i+228>>2],e[i+232>>2],e[i+236>>2]),f=e[i+208>>2],E=e[i+212>>2],r=e[i+220>>2],t=e[i+216>>2];break s}if(t=27+(z(L,-3)+zA|0)|0,!((r=e[i+784>>2])>>>t|0&&(0|t)<=30)){Pa(i+352|0,ve),si(i+336|0,r),PA(i+320|0,e[i+352>>2],e[i+356>>2],e[i+360>>2],e[i+364>>2],e[i+336>>2],e[i+340>>2],e[i+344>>2],e[i+348>>2]),Pa(i+304|0,e[124648+(L<<2)>>2]),PA(i+288|0,e[i+320>>2],e[i+324>>2],e[i+328>>2],e[i+332>>2],e[i+304>>2],e[i+308>>2],e[i+312>>2],e[i+316>>2]),f=e[i+288>>2],E=e[i+292>>2],r=e[i+300>>2],t=e[i+296>>2];break s}}for(;!e[(i+784|0)+((T=(r=T)-1|0)<<2)>>2];);if(ee=0,t=(0|L)%9|0){if(a=0,t=(0|L)<0?t+9|0:t,r){for(m=1e9/(0|(p=e[124720+(0-t<<2)>>2]))|0,I=0,c=0;f=(f=I)+(T=((E=e[(I=(i+784|0)+(c<<2)|0)>>2])>>>0)/(p>>>0)|0)|0,e[I>>2]=f,a=(f=!f&(0|a)==(0|c))?a+1&2047:a,L=f?L-9|0:L,I=z(m,E-z(p,T)|0),(0|(c=c+1|0))!=(0|r););I&&(e[(i+784|0)+(r<<2)>>2]=I,r=r+1|0)}else r=0;L=9+(L-t|0)|0}else a=0;for(;;){c=(i+784|0)+(a<<2)|0;i:{for(;;){if(((0|L)!=36|ue[c>>2]>=10384593)&(0|L)>=36)break i;for(T=r+2047|0,I=0,t=r;r=t,p=I,I=(t=e[(T=(i+784|0)+((f=2047&T)<<2)|0)>>2])<<29,t=E=t>>>3|0,!(m=(p=p+I|0)>>>0<I>>>0?t+1|0:t)&p>>>0<1000000001?I=0:p=(t=p)-It(I=gi(t,m,1e9),$,1e9,0)|0,e[T>>2]=p,t=(0|f)!=(r-1&2047)||(0|a)==(0|f)||p?r:f,T=f-1|0,(0|a)!=(0|f););if(ee=ee-29|0,I)break}(0|(a=a-1&2047))==(0|t)&&(c=r=(p=i+784|0)+((t+2046&2047)<<2)|0,m=e[r>>2],r=t-1&2047,e[c>>2]=m|e[p+(r<<2)>>2]),L=L+9|0,e[(i+784|0)+(a<<2)>>2]=I;continue}break}i:{c:for(;;){for(p=r+1&2047,I=(i+784|0)+((r-1&2047)<<2)|0;;){f=(0|L)>45?9:1;o:{for(;;){t=a,c=0;u:{for(;;){if((0|(a=t+c&2047))!=(0|r)&&!((a=e[(i+784|0)+(a<<2)>>2])>>>0<(m=e[124672+(c<<2)>>2])>>>0)){if(a>>>0>m>>>0)break u;if((0|(c=c+1|0))!=4)continue}break}if((0|L)==36){for(p=0,m=0,c=0,f=0,E=0;(0|(a=t+c&2047))==(0|r)&&(e[780+(i+((r=r+1&2047)<<2)|0)>>2]=0),si(i+768|0,e[(i+784|0)+(a<<2)>>2]),PA(i+752|0,p,m,f,E,0,0,1342177280,1075633366),$r(i+736|0,e[i+752>>2],e[i+756>>2],e[i+760>>2],e[i+764>>2],e[i+768>>2],e[i+772>>2],e[i+776>>2],e[i+780>>2]),f=e[i+744>>2],E=e[i+748>>2],p=e[i+736>>2],m=e[i+740>>2],(0|(c=c+1|0))!=4;);if(Pa(i+720|0,ve),PA(i+704|0,p,m,f,E,e[i+720>>2],e[i+724>>2],e[i+728>>2],e[i+732>>2]),f=e[i+712>>2],E=e[i+716>>2],p=0,m=0,I=e[i+704>>2],Z=e[i+708>>2],(0|(a=(T=(0|(c=(o=ee+113|0)-MA|0))<(0|zA))?(0|c)>0?c:0:zA))<=112)break o;break i}}if(ee=f+ee|0,a=r,(0|t)!=(0|r))break}for(E=1e9>>>f|0,T=~(-1<<f),c=0,a=t;m=(m=c)+((o=e[(c=(i+784|0)+(t<<2)|0)>>2])>>>f|0)|0,e[c>>2]=m,a=(m=!m&(0|t)==(0|a))?a+1&2047:a,L=m?L-9|0:L,c=z(E,o&T),(0|r)!=(0|(t=t+1&2047)););if(!c)continue;if((0|a)!=(0|p)){e[(i+784|0)+(r<<2)>>2]=c,r=p;continue c}e[I>>2]=1|e[I>>2];continue}break}break}Nr(i+656|0,Ws(1,225-a|0)),zn(i+688|0,e[i+656>>2],e[i+660>>2],e[i+664>>2],e[i+668>>2],I,Z,f,E),Ke=e[i+696>>2],rA=e[i+700>>2],Qe=e[i+688>>2],We=e[i+692>>2],Nr(i+640|0,Ws(1,113-a|0)),hi(i+672|0,I,Z,f,E,e[i+640>>2],e[i+644>>2],e[i+648>>2],e[i+652>>2]),Wi(i+624|0,I,Z,f,E,p=e[i+672>>2],m=e[i+676>>2],ce=e[i+680>>2],he=e[i+684>>2]),$r(i+608|0,Qe,We,Ke,rA,e[i+624>>2],e[i+628>>2],e[i+632>>2],e[i+636>>2]),f=e[i+616>>2],E=e[i+620>>2],I=e[i+608>>2],Z=e[i+612>>2]}if((0|(L=t+4&2047))!=(0|r)){i:if((L=e[(i+784|0)+(L<<2)>>2])>>>0<=499999999){if(!L&(t+5&2047)==(0|r))break i;Nr(i+496|0,.25*+(0|ve)),$r(i+480|0,p,m,ce,he,e[i+496>>2],e[i+500>>2],e[i+504>>2],e[i+508>>2]),ce=e[i+488>>2],he=e[i+492>>2],p=e[i+480>>2],m=e[i+484>>2]}else(0|L)==5e8?(dt=+(0|ve),(t+5&2047)!=(0|r)?(Nr(i+560|0,.75*dt),$r(i+544|0,p,m,ce,he,e[i+560>>2],e[i+564>>2],e[i+568>>2],e[i+572>>2]),ce=e[i+552>>2],he=e[i+556>>2],p=e[i+544>>2],m=e[i+548>>2]):(Nr(i+528|0,.5*dt),$r(i+512|0,p,m,ce,he,e[i+528>>2],e[i+532>>2],e[i+536>>2],e[i+540>>2]),ce=e[i+520>>2],he=e[i+524>>2],p=e[i+512>>2],m=e[i+516>>2])):(Nr(i+592|0,.75*+(0|ve)),$r(i+576|0,p,m,ce,he,e[i+592>>2],e[i+596>>2],e[i+600>>2],e[i+604>>2]),ce=e[i+584>>2],he=e[i+588>>2],p=e[i+576>>2],m=e[i+580>>2]);(0|a)>111||(hi(i+464|0,p,m,ce,he,0,0,0,1073676288),gA(e[i+464>>2],e[i+468>>2],e[i+472>>2],e[i+476>>2],0,0,0,0)||($r(i+448|0,p,m,ce,he,0,0,0,1073676288),ce=e[i+456>>2],he=e[i+460>>2],p=e[i+448>>2],m=e[i+452>>2]))}$r(i+432|0,I,Z,f,E,p,m,ce,he),Wi(i+416|0,e[i+432>>2],e[i+436>>2],e[i+440>>2],e[i+444>>2],Qe,We,Ke,rA),f=e[i+424>>2],E=e[i+428>>2],I=e[i+416>>2],Z=e[i+420>>2],(at-2|0)>=(2147483647&o)||(e[i+408>>2]=f,e[i+412>>2]=2147483647&E,e[i+400>>2]=I,e[i+404>>2]=Z,PA(i+384|0,I,Z,f,E,0,0,0,1073610752),f=(t=(0|(t=l0(e[i+400>>2],e[i+404>>2],e[i+408>>2],e[i+412>>2],1081081856)))>=0)?e[i+392>>2]:f,E=t?e[i+396>>2]:E,I=t?e[i+384>>2]:I,Z=t?e[i+388>>2]:Z,ee=t+ee|0,!(!!(0|gA(p,m,ce,he,0,0,0,0))&(t?T&(0|a)!=(0|c):T))&(ee+110|0)<=(0|at)||(e[56798]=68)),cr(i+368|0,I,Z,f,E,ee),f=e[i+368>>2],E=e[i+372>>2],r=e[i+380>>2],t=e[i+376>>2]}else Pa(i+48|0,ve),si(i+32|0,t),PA(i+16|0,e[i+48>>2],e[i+52>>2],e[i+56>>2],e[i+60>>2],e[i+32>>2],e[i+36>>2],e[i+40>>2],e[i+44>>2]),f=e[i+16>>2],E=e[i+20>>2],r=e[i+28>>2],t=e[i+24>>2];else Nr(i,0*+(0|ve)),f=e[i>>2],E=e[i+4>>2],r=e[i+12>>2],t=e[i+8>>2]}e[s+40>>2]=t,e[s+44>>2]=r,e[s+32>>2]=f,e[s+36>>2]=E,J=i+8976|0,p=e[s+40>>2],m=e[s+44>>2],f=e[s+32>>2],E=e[s+36>>2];break e;case 3:break r}(0|(r=e[t+116>>2]))>0|(0|r)>=0&&(e[t+4>>2]=e[t+4>>2]-1);break A}if((0|(r=e[t+4>>2]))==e[t+104>>2]?r=bA(t):(e[t+4>>2]=r+1,r=l[0|r]),(0|r)!=40){if(m=2147450880,e[t+116>>2]<0)break e;e[t+4>>2]=e[t+4>>2]-1;break e}for(c=1;(0|(r=e[t+4>>2]))==e[t+104>>2]?r=bA(t):(e[t+4>>2]=r+1,r=l[0|r]),r-48>>>0<10|r-65>>>0<26|(0|r)==95||!(r-97>>>0>=26);)c=c+1|0;if(m=2147450880,(0|r)==41)break e;(0|(r=e[t+116>>2]))>0|(0|r)>=0&&(e[t+4>>2]=e[t+4>>2]-1);r:{if(a){if(c)break r;break e}break A}for(;c=c-1|0,(0|r)>0|(0|r)>=0&&(e[t+4>>2]=e[t+4>>2]-1),c;);break e}e[56798]=28,Ja(t,0,0)}m=0}e[A>>2]=f,e[A+4>>2]=E,e[A+8>>2]=p,e[A+12>>2]=m,J=s+48|0}function QA(A,t,r,a,s){var i,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0,MA=0,zA=0;if(J=i=J-112|0,e[r+8>>2]){MA=e[50754]/70|0,Z=(I=e[a+12>>2])||256;e:if((0|t)!=2){if((0|t)==1){A:if(l[A+11|0]!=3)switch(l[a-15|0]-3|0){case 0:case 5:break A;default:break e}Z=(0|(I=e[e[47192]+44>>2]))<(0|Z)?Z:I}}else{if((0|(I=e[e[47192]+80>>2]))<=0|!(8&l[0|a]|I>>>0<=l[A+14|0]|32&l[A+6|0]))break e;MA<<=1}if(e[36436]=0,he=A,zA=t,p=a,J=L=J-16|0,A=e[34460]+e[r+8>>2]|0,t=(t=l[A+2|0])>>>0>=24?24:t,e[L+12>>2]=t,c=e[r+12>>2]+e[r+24>>2]|0,e[36422]=c,t){for(I=A+4|0,o=1&b[A+4>>1];A=145488+(m<<3)|0,a=I+(o?m<<6:z(m,44))|0,e[A+4>>2]=a,T=M[a>>1],b[A+2>>1]=T,b[A>>1]=l[a+16|0],f=2&T?m:f,(0|(m=m+1|0))!=(0|t););I=145488,(0|f)<=0||((0|zA)!=1?(t=t-f|0,e[L+12>>2]=t,I=145488+(f<<3)|0):(t=f+1|0,e[L+12>>2]=t,I=145488))}else t=0,I=145488;if(!(!e[r+4>>2]|e[r+20>>2]|l[he+11|0]!=2)){if(o=e[r+36>>2],a=e[r+40>>2],A=0,(0|(t=e[L+12>>2]))>=2){f=o>>>12|0,T=a>>>26&7,ve=a>>>18&248,ee=z(ce=63&a,50),We=63&(Ke=o>>>6|0),m=o<<1&126,rA=z(a>>>16&31,50)-750|0,cA=z(a>>>11&31,50)-750|0,Qe=z(a>>>6&31,50)-750|0;e:{A:if((0|zA)!=1){if(!(f|ce))break e;if(8&f?(t=e[4+(I+((c=t-1|0)<<3)|0)>>2],b[t>>1]<0?A=t:(A=(0|(A=e[44469]+1|0))<=169?A:0,e[44469]=A,c=M[t+20>>1]|M[t+22>>1]<<16,A=177888+(A<<6)|0,o=M[t+16>>1]|M[t+18>>1]<<16,b[A+16>>1]=o,b[A+18>>1]=o>>>16,b[A+20>>1]=c,b[A+22>>1]=c>>>16,c=M[t+4>>1]|M[t+6>>1]<<16,o=M[t>>1]|M[t+2>>1]<<16,b[A>>1]=o,b[A+2>>1]=o>>>16,b[A+4>>1]=c,b[A+6>>1]=c>>>16,c=M[t+12>>1]|M[t+14>>1]<<16,o=M[t+8>>1]|M[t+10>>1]<<16,b[A+8>>1]=o,b[A+10>>1]=o>>>16,b[A+12>>1]=c,b[A+14>>1]=c>>>16,c=M[t+28>>1]|M[t+30>>1]<<16,o=M[t+24>>1]|M[t+26>>1]<<16,b[A+24>>1]=o,b[A+26>>1]=o>>>16,b[A+28>>1]=c,b[A+30>>1]=c>>>16,c=M[t+36>>1]|M[t+38>>1]<<16,o=M[t+32>>1]|M[t+34>>1]<<16,b[A+32>>1]=o,b[A+34>>1]=o>>>16,b[A+36>>1]=c,b[A+38>>1]=c>>>16,c=M[t+44>>1]|M[t+46>>1]<<16,o=M[t+40>>1]|M[t+42>>1]<<16,b[A+40>>1]=o,b[A+42>>1]=o>>>16,b[A+44>>1]=c,b[A+46>>1]=c>>>16,c=M[t+52>>1]|M[t+54>>1]<<16,o=M[t+48>>1]|M[t+50>>1]<<16,b[A+48>>1]=o,b[A+50>>1]=o>>>16,b[A+52>>1]=c,b[A+54>>1]=c>>>16,c=M[t+60>>1]|M[t+62>>1]<<16,t=M[t+56>>1]|M[t+58>>1]<<16,b[A+56>>1]=t,b[A+58>>1]=t>>>16,b[A+60>>1]=c,b[A+62>>1]=c>>>16,g[A+16|0]=0,b[A>>1]=32768|M[A>>1],c=e[L+12>>2]-1|0),e[4+(I+(c<<3)|0)>>2]=A,t=1792,(0|(c=b[A+4>>1]))<300||(t=1536,c>>>0<400||(t=c>>>0<500?1280:1024)),e[36436]=t,o=35):(e[L+12>>2]=t+1,b[(A=(c=I+(t<<3)|0)-8|0)>>1]=m,t=e[A+4>>2],A=(0|(A=e[44469]+1|0))<=169?A:0,e[44469]=A,(A=177888+(Ke=A<<6)|0)&&(o=M[t+4>>1]|M[t+6>>1]<<16,E=M[t>>1]|M[t+2>>1]<<16,b[A>>1]=E,b[A+2>>1]=E>>>16,b[A+4>>1]=o,b[A+6>>1]=o>>>16,o=M[t+60>>1]|M[t+62>>1]<<16,E=M[t+56>>1]|M[t+58>>1]<<16,b[A+56>>1]=E,b[A+58>>1]=E>>>16,b[A+60>>1]=o,b[A+62>>1]=o>>>16,o=M[t+52>>1]|M[t+54>>1]<<16,E=M[t+48>>1]|M[t+50>>1]<<16,b[A+48>>1]=E,b[A+50>>1]=E>>>16,b[A+52>>1]=o,b[A+54>>1]=o>>>16,o=M[t+44>>1]|M[t+46>>1]<<16,E=M[t+40>>1]|M[t+42>>1]<<16,b[A+40>>1]=E,b[A+42>>1]=E>>>16,b[A+44>>1]=o,b[A+46>>1]=o>>>16,o=M[t+36>>1]|M[t+38>>1]<<16,E=M[t+32>>1]|M[t+34>>1]<<16,b[A+32>>1]=E,b[A+34>>1]=E>>>16,b[A+36>>1]=o,b[A+38>>1]=o>>>16,o=M[t+28>>1]|M[t+30>>1]<<16,E=M[t+24>>1]|M[t+26>>1]<<16,b[A+24>>1]=E,b[A+26>>1]=E>>>16,b[A+28>>1]=o,b[A+30>>1]=o>>>16,o=M[t+20>>1]|M[t+22>>1]<<16,E=M[t+16>>1]|M[t+18>>1]<<16,b[A+16>>1]=E,b[A+18>>1]=E>>>16,b[A+20>>1]=o,b[A+22>>1]=o>>>16,o=M[t+12>>1]|M[t+14>>1]<<16,t=M[t+8>>1]|M[t+10>>1]<<16,b[A+8>>1]=t,b[A+10>>1]=t>>>16,b[A+12>>1]=o,b[A+14>>1]=o>>>16,g[Ke+177904|0]=0,b[A>>1]=32768|M[A>>1]),b[c>>1]=0,e[c+4>>2]=A,m>>>0>=37&&(e[36422]=(m+e[36422]|0)-36),o=We<<1,ce&&He(A,ee,Qe,cA,T,rA,ve,f)),e[e[32972]+132>>2]||(t=l[A+17|0])&&(t=b[102896+(((0|(t=(o<<6>>>0)/(t>>>0)|0))>=199?199:t)<<1)>>1],g[A+18|0]=(0|z(t,l[A+18|0]))/512,g[A+19|0]=(0|z(t,l[A+19|0]))/512,g[A+20|0]=(0|z(t,l[A+20|0]))/512,g[A+21|0]=(0|z(t,l[A+21|0]))/512,g[A+22|0]=(0|z(t,l[A+22|0]))/512,g[A+23|0]=(0|z(t,l[A+23|0]))/512,g[A+24|0]=(0|z(t,l[A+24|0]))/512,g[A+25|0]=(0|z(t,l[A+25|0]))/512),a-536870912>>>0<=1073741823){if(T=e[44469],(0|(c=e[L+12>>2]))>0)for(A=z(a>>>29|0,10)+102854|0,ve=b[A+4>>1],ce=b[A+2>>1],ee=b[A>>1],rA=b[A+6>>1],cA=b[A+8>>1],o=0;t=e[4+(Qe=I+(o<<3)|0)>>2],b[t>>1]<0?A=t:(A=177888+(We=(T=(0|(A=T+1|0))<=169?A:0)<<6)|0)?(a=M[t+4>>1]|M[t+6>>1]<<16,c=M[t>>1]|M[t+2>>1]<<16,b[A>>1]=c,b[A+2>>1]=c>>>16,b[A+4>>1]=a,b[A+6>>1]=a>>>16,a=M[t+60>>1]|M[t+62>>1]<<16,c=M[t+56>>1]|M[t+58>>1]<<16,b[A+56>>1]=c,b[A+58>>1]=c>>>16,b[A+60>>1]=a,b[A+62>>1]=a>>>16,a=M[t+52>>1]|M[t+54>>1]<<16,c=M[t+48>>1]|M[t+50>>1]<<16,b[A+48>>1]=c,b[A+50>>1]=c>>>16,b[A+52>>1]=a,b[A+54>>1]=a>>>16,a=M[t+44>>1]|M[t+46>>1]<<16,c=M[t+40>>1]|M[t+42>>1]<<16,b[A+40>>1]=c,b[A+42>>1]=c>>>16,b[A+44>>1]=a,b[A+46>>1]=a>>>16,a=M[t+36>>1]|M[t+38>>1]<<16,c=M[t+32>>1]|M[t+34>>1]<<16,b[A+32>>1]=c,b[A+34>>1]=c>>>16,b[A+36>>1]=a,b[A+38>>1]=a>>>16,a=M[t+28>>1]|M[t+30>>1]<<16,c=M[t+24>>1]|M[t+26>>1]<<16,b[A+24>>1]=c,b[A+26>>1]=c>>>16,b[A+28>>1]=a,b[A+30>>1]=a>>>16,a=M[t+20>>1]|M[t+22>>1]<<16,c=M[t+16>>1]|M[t+18>>1]<<16,b[A+16>>1]=c,b[A+18>>1]=c>>>16,b[A+20>>1]=a,b[A+22>>1]=a>>>16,a=M[t+12>>1]|M[t+14>>1]<<16,t=M[t+8>>1]|M[t+10>>1]<<16,b[A+8>>1]=t,b[A+10>>1]=t>>>16,b[A+12>>1]=a,b[A+14>>1]=a>>>16,g[We+177904|0]=0,b[A>>1]=32768|M[A>>1],c=e[L+12>>2]):A=0,e[Qe+4>>2]=A,b[A+8>>1]=(0|z(ve,b[A+8>>1]))/256,b[A+6>>1]=(0|z(ce,b[A+6>>1]))/256,b[A+4>>1]=(0|z(ee,b[A+4>>1]))/256,b[A+12>>1]=(0|z(cA,b[A+12>>1]))/256,b[A+10>>1]=(0|z(rA,b[A+10>>1]))/256,(0|c)>(0|(o=o+1|0)););e[44469]=T}if(!A)break e}else{t=e[I+4>>2],(0|(c=b[t>>1]))<0?A=t:(a=(0|(a=e[44469]+1|0))<=169?a:0,e[44469]=a,(a=177888+(c=a<<6)|0)&&(A=M[t+4>>1]|M[t+6>>1]<<16,E=M[t>>1]|M[t+2>>1]<<16,b[a>>1]=E,b[a+2>>1]=E>>>16,b[a+4>>1]=A,b[a+6>>1]=A>>>16,A=M[t+60>>1]|M[t+62>>1]<<16,E=M[t+56>>1]|M[t+58>>1]<<16,b[a+56>>1]=E,b[a+58>>1]=E>>>16,b[a+60>>1]=A,b[a+62>>1]=A>>>16,A=M[t+52>>1]|M[t+54>>1]<<16,E=M[t+48>>1]|M[t+50>>1]<<16,b[a+48>>1]=E,b[a+50>>1]=E>>>16,b[a+52>>1]=A,b[a+54>>1]=A>>>16,A=M[t+44>>1]|M[t+46>>1]<<16,E=M[t+40>>1]|M[t+42>>1]<<16,b[a+40>>1]=E,b[a+42>>1]=E>>>16,b[a+44>>1]=A,b[a+46>>1]=A>>>16,A=M[t+36>>1]|M[t+38>>1]<<16,E=M[t+32>>1]|M[t+34>>1]<<16,b[a+32>>1]=E,b[a+34>>1]=E>>>16,b[a+36>>1]=A,b[a+38>>1]=A>>>16,A=M[t+28>>1]|M[t+30>>1]<<16,E=M[t+24>>1]|M[t+26>>1]<<16,b[a+24>>1]=E,b[a+26>>1]=E>>>16,b[a+28>>1]=A,b[a+30>>1]=A>>>16,A=M[t+20>>1]|M[t+22>>1]<<16,E=M[t+16>>1]|M[t+18>>1]<<16,b[a+16>>1]=E,b[a+18>>1]=E>>>16,b[a+20>>1]=A,b[a+22>>1]=A>>>16,A=M[t+12>>1]|M[t+14>>1]<<16,t=M[t+8>>1]|M[t+10>>1]<<16,b[a+8>>1]=t,b[a+10>>1]=t>>>16,b[a+12>>1]=A,b[a+14>>1]=A>>>16,g[c+177904|0]=0,c=-32768|M[a>>1],b[a>>1]=c,A=a)),e[I+4>>2]=A,b[I>>1]=m||50,b[I+2>>1]=16384|M[I+2>>1],b[A>>1]=16384|c,c=e[I+12>>2],t=l[c+17|0],a=e[32972],e[a+132>>2]&&(g[A+39|0]=l[c+39|0]-4);r:if(ce){if(2048&o){t=(z(t,31&Ke)>>>0)/30|0,e[a+132>>2]||(a=l[A+17|0])&&(t=b[102896+(((0|(t=(t<<6>>>0)/(a>>>0)|0))>=199?199:t)<<1)>>1],g[A+18|0]=(0|z(t,l[A+18|0]))/512,g[A+19|0]=(0|z(t,l[A+19|0]))/512,g[A+20|0]=(0|z(t,l[A+20|0]))/512,g[A+21|0]=(0|z(t,l[A+21|0]))/512,g[A+22|0]=(0|z(t,l[A+22|0]))/512,g[A+23|0]=(0|z(t,l[A+23|0]))/512,g[A+24|0]=(0|z(t,l[A+24|0]))/512,g[A+25|0]=(0|z(t,l[A+25|0]))/512),He(A,ee,Qe,cA,T,rA,ve,f);break r}if(He(A,ee,Qe,cA,T,rA,ve,f),e[e[32972]+132>>2]||!(t=l[A+17|0]))break r;t=b[102896+(((0|(t=(We<<7>>>0)/(t>>>0)|0))>=199?199:t)<<1)>>1],g[A+18|0]=(0|z(t,l[A+18|0]))/512,g[A+19|0]=(0|z(t,l[A+19|0]))/512,g[A+20|0]=(0|z(t,l[A+20|0]))/512,g[A+21|0]=(0|z(t,l[A+21|0]))/512,g[A+22|0]=(0|z(t,l[A+22|0]))/512,g[A+23|0]=(0|z(t,l[A+23|0]))/512,g[A+24|0]=(0|z(t,l[A+24|0]))/512,g[A+25|0]=(0|z(t,l[A+25|0]))/512}else if(a=e[a+132>>2],8&f){if(a||!(a=l[A+17|0]))break r;t=((16320&z(t,48))>>>0)/(a>>>0)|0,t=b[102896+((t>>>0>=199?199:t)<<1)>>1],g[A+18|0]=(0|z(t,l[A+18|0]))/512,g[A+19|0]=(0|z(t,l[A+19|0]))/512,g[A+20|0]=(0|z(t,l[A+20|0]))/512,g[A+21|0]=(0|z(t,l[A+21|0]))/512,g[A+22|0]=(0|z(t,l[A+22|0]))/512,g[A+23|0]=(0|z(t,l[A+23|0]))/512,g[A+24|0]=(0|z(t,l[A+24|0]))/512,g[A+25|0]=(0|z(t,l[A+25|0]))/512}else a||(t=l[A+17|0])&&(t=b[102896+(((t=1792/(t>>>0)|0)>>>0>=199?199:t)<<1)>>1],g[A+18|0]=(0|z(t,l[A+18|0]))/512,g[A+19|0]=(0|z(t,l[A+19|0]))/512,g[A+20|0]=(0|z(t,l[A+20|0]))/512,g[A+21|0]=(0|z(t,l[A+21|0]))/512,g[A+22|0]=(0|z(t,l[A+22|0]))/512,g[A+23|0]=(0|z(t,l[A+23|0]))/512,g[A+24|0]=(0|z(t,l[A+24|0]))/512,g[A+25|0]=(0|z(t,l[A+25|0]))/512);if(!(8&f))break A;t=2816,(0|(a=b[A+4>>1]))<300||(t=2560,a>>>0<400||(t=a>>>0<500?2304:2048)),e[36436]=t}4&f&&(b[A>>1]=32|M[A>>1]),2&f&&(b[A>>1]=16|M[A>>1])}64&f&&dr(20,0),A=m&f<<27>>31}else A=0;c=A+e[36422]|0,e[36422]=c,t=e[L+12>>2]}if((0|(a=t-1|0))<=0)o=0;else{if(A=0,m=0,o=0,t-2>>>0>=3)for(ve=-4&a,T=0;o=(((b[(f=m<<3)+I>>1]+o|0)+b[I+(8|f)>>1]|0)+b[I+(16|f)>>1]|0)+b[I+(24|f)>>1]|0,m=m+4|0,(0|ve)!=(0|(T=T+4|0)););if(f=3&a)for(;o=b[I+(m<<3)>>1]+o|0,m=m+1|0,(0|f)!=(0|(A=A+1|0)););}if(A=t,(f=e[r+20>>2])&&(A=a,m=f+e[34460]|0,(T=l[m+2|0])&&(ce=M[m+4>>1],b[I+(a<<3)>>1]=l[m+20|0],f=1,A=t,(0|T)!=1))){if(ve=m+4|0,ce&=1,rA=1&(m=T-1|0),(0|T)!=2)for(cA=-2&m,T=0;Qe=ve+(f<<6)|0,We=ve+z(f,44)|0,Ke=l[(ee=ce?Qe:We)+16|0],e[4+(m=I+(A<<3)|0)>>2]=ee,b[m>>1]=Ke,b[m+2>>1]=M[ee>>1],Qe=l[16+(ee=ce?Qe- -64|0:We+44|0)|0],e[m+12>>2]=ee,b[m+8>>1]=Qe,b[m+10>>1]=M[ee>>1],f=f+2|0,A=A+2|0,(0|cA)!=(0|(T=T+2|0)););rA&&(f=ve+(ce?f<<6:z(f,44))|0,T=l[f+16|0],e[4+(m=I+(A<<3)|0)>>2]=f,b[m>>1]=T,b[m+2>>1]=M[f>>1],A=A+1|0)}e:if(!((0|o)<=0)){A:{switch(zA-1|0){case 1:if(f=(0|(f=(e[r+44>>2]+c|0)-45|0))<=10?10:f,8&l[0|p]&&(f=f+(l[e[36128]+14|0]<<1)|0),(0|a)<=0)break e;if(p=(f<<8)/(0|o)|0,m=0,(0|t)!=2)for(t=-2&a,f=0;b[(o=(c=m<<3)+I|0)>>1]=(0|z(p,b[o>>1]))/256,b[(c=I+(8|c)|0)>>1]=(0|z(p,b[c>>1]))/256,m=m+2|0,(0|t)!=(0|(f=f+2|0)););if(!(1&a))break e;b[(t=I+(m<<3)|0)>>1]=(0|z(p,b[t>>1]))/256;break e;case 0:if(e[r>>2]!=1||(0|(f=e[r+44>>2]))>129)break A;b[I>>1]=(0|z(f,b[I>>1]))/130;break A}(0|(f=e[r+44>>2]))<=0||(c=(f-o|0)+c|0,e[36422]=c)}if(!(!c|(0|a)<=0)){if(p=(o+c<<8)/(0|o)|0,m=0,(0|t)!=2)for(t=-2&a,f=0;b[(o=(c=m<<3)+I|0)>>1]=(0|z(p,b[o>>1]))/256,b[(c=I+(8|c)|0)>>1]=(0|z(p,b[c>>1]))/256,m=m+2|0,(0|t)!=(0|(f=f+2|0)););1&a&&(b[(t=I+(m<<3)|0)>>1]=(0|z(p,b[t>>1]))/256)}}if(e[i+108>>2]=A,J=L+16|0,I){if((0|(A=e[r+16>>2]))!=e[36438]&&(e[36438]=A,t=216192+(e[50758]<<4)|0,e[t>>2]=14,e[t+4>>2]=A,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0),m=(A=e[e[32972]+132>>2])?1:3,t=e[I+4>>2],e[r+28>>2]|!l[145748]||(g[145748]=0,m=A?2:4),(a=e[36426])&&(!((2&(A=M[a>>1]))>>>1|l[a+16|0]<2)|16&A||(c=216192+(e[36439]<<4)|0,e[c+12>>2]=t,8&A&&(A=(0|(A=e[44469]+1|0))<=169?A:0,e[44469]=A,(A=177888+(f=A<<6)|0)&&(p=M[t+4>>1]|M[t+6>>1]<<16,o=M[t>>1]|M[t+2>>1]<<16,b[A>>1]=o,b[A+2>>1]=o>>>16,b[A+4>>1]=p,b[A+6>>1]=p>>>16,p=M[t+60>>1]|M[t+62>>1]<<16,o=M[t+56>>1]|M[t+58>>1]<<16,b[A+56>>1]=o,b[A+58>>1]=o>>>16,b[A+60>>1]=p,b[A+62>>1]=p>>>16,p=M[t+52>>1]|M[t+54>>1]<<16,o=M[t+48>>1]|M[t+50>>1]<<16,b[A+48>>1]=o,b[A+50>>1]=o>>>16,b[A+52>>1]=p,b[A+54>>1]=p>>>16,p=M[t+44>>1]|M[t+46>>1]<<16,o=M[t+40>>1]|M[t+42>>1]<<16,b[A+40>>1]=o,b[A+42>>1]=o>>>16,b[A+44>>1]=p,b[A+46>>1]=p>>>16,p=M[t+36>>1]|M[t+38>>1]<<16,o=M[t+32>>1]|M[t+34>>1]<<16,b[A+32>>1]=o,b[A+34>>1]=o>>>16,b[A+36>>1]=p,b[A+38>>1]=p>>>16,p=M[t+28>>1]|M[t+30>>1]<<16,o=M[t+24>>1]|M[t+26>>1]<<16,b[A+24>>1]=o,b[A+26>>1]=o>>>16,b[A+28>>1]=p,b[A+30>>1]=p>>>16,p=M[t+20>>1]|M[t+22>>1]<<16,o=M[t+16>>1]|M[t+18>>1]<<16,b[A+16>>1]=o,b[A+18>>1]=o>>>16,b[A+20>>1]=p,b[A+22>>1]=p>>>16,p=M[t+12>>1]|M[t+14>>1]<<16,o=M[t+8>>1]|M[t+10>>1]<<16,b[A+8>>1]=o,b[A+10>>1]=o>>>16,b[A+12>>1]=p,b[A+14>>1]=p>>>16,g[f+177904|0]=0,b[A>>1]=32768|M[A>>1]),b[8+(f=f+177888|0)>>1]=M[a+8>>1],g[f+21|0]=l[a+21|0],b[f+10>>1]=M[a+10>>1],g[f+22|0]=l[a+22|0],b[f+12>>1]=M[a+12>>1],g[f+23|0]=l[a+23|0],b[f+14>>1]=M[a+14>>1],g[f+24|0]=l[a+24|0],g[f+25|0]=l[a+25|0],e[c+12>>2]=A))),(0|zA)!=2|l[he+11|0]!=2||(Oa(),e[36427]=e[50758]),!((0|(he=e[i+108>>2]))<2)){for(A=e[36433],p=(z(256-A|0,Z)+(A<<8)|0)/256|0,A=e[36432],c=(z(256-A|0,Z)+(A<<8)|0)/256|0,o=e[50754],A=0,a=1;f=M[2+(L=(I+(a<<3)|0)-8|0)>>1],f=(0|z((0|z(o,b[L>>1]))/1e3|0,4&f?c:16384&f?p:Z))/256|0,e[(a<<2)+i>>2]=f,A=A+f|0,(0|he)!=(0|(a=a+1|0)););if(!((0|A)<=0|(0|A)>=(0|MA)|(0|he)<2)){if(a=1,p=1&(f=he-1|0),(0|he)!=2)for(c=-2&f,Z=0;e[(f=(a<<2)+i|0)>>2]=(0|z(e[f>>2],MA))/(0|A),e[f+4>>2]=(0|z(e[f+4>>2],MA))/(0|A),a=a+2|0,(0|c)!=(0|(Z=Z+2|0)););p&&(e[(a=(a<<2)+i|0)>>2]=(0|z(e[a>>2],MA))/(0|A))}if(Z=0,!((0|he)<2))for(he=zA+256|0,a=1;A=e[4+(I+(a<<3)|0)>>2],!(f=e[r+28>>2])|128&l[0|t]||(e[36422]=0,vs(f,he,0,o=e[r>>2],0,p=(p=e[r+32>>2])?(p<<5)/100|0:32),g[145748]=1,e[r+28>>2]=0),(0|s)<0||(s=64&l[0|t]?6:s,(e[i+108>>2]-1|0)==(0|a)&&(s=(f=s)|(3840&(s=e[36436])?s:0))),f=e[(a<<2)+i>>2],e[36440]=f+e[36440],e[36441]=f+e[36441],f?(p=e[50758],e[36439]=p,(0|s)>=0&&(e[(p=216192+(p<<4)|0)>>2]=m,e[p+12>>2]=A,e[p+8>>2]=t,e[p+4>>2]=f+(s<<16),t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0),e[36426]=A,Z=f+Z|0):e[36426]=0,t=A,(0|(a=a+1|0))<e[i+108>>2];);}!e[36438]|(0|zA)==1||(e[36438]=0,A=216192+(e[50758]<<4)|0,e[A>>2]=14,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0)}}J=i+112|0}function TA(A,t,r,a){var s,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0,MA=0,zA=0;J=s=J-1856|0,e[s+164>>2]=0,r?E=e[r>>2]:jA(r=s- -64|0,0,96),e[33264]=0,e[s+1824>>2]=0,e[s+1828>>2]=0,e[s+1832>>2]=0,e[s+1836>>2]=0,g[s+1616|0]=0,g[s+992|0]=0,g[s+1200|0]=0,g[s+784|0]=0;e:if(e[A+688>>2]){for(e[s+1840>>2]=t,l[0|(i=t)]==32&&(i=t+1|0,e[s+1840>>2]=i),T=s+416|1,Ke=s+1844|1,cA=s+1848|1,MA=s+1852|1,e[s+1820>>2]=i,Gt(s+168|0,i),i=e[s+1820>>2];(32|l[0|i])!=32;)i=Gt(s+164|0,i)+e[s+1820>>2]|0,e[s+1820>>2]=i,ee=ee+1|0;wt(s+256|0,t,rA=(0|(c=i-t|0))>=159?159:c),!(Qe=4194304&E)|(0|ee)!=1?(he=((0|(i=e[47202]))==36)<<2,(0|ee)==1|(0|i)!=36||(i=e[s+1840>>2]-1|0,e[s+1840>>2]=i,g[0|i]=95,he=0,o=!!(0|kr(A,s+1840|0,s+1616|0,s+1832|0,0,r)),i=e[47202])):(o=1,Gt(s+172|0,i+1|0),rr(e[s+172>>2])&&(0|Hs(e[s+164>>2]))==(0|Hs(e[s+172>>2]))&&(o=0),he=(0|(i=e[47202]))==36?4:o,o=0);A:{r:{s:{i:{c:if(16&i)L=15&i,he=0;else{if(ce=1,o||(ce=!!(0|kr(A,s+1840|0,s+1616|0,s+1832|0,2,r))),50331648&(i=e[s+1832>>2])&&(c=e[s+1820>>2],l[c+1|0]==46&&(g[c+1|0]=32,i=e[s+1832>>2])),536870912&i){if(!a)break e;qA(a,e[s+1840>>2]);break e}if(8192&i|!(128&i)|ce)a=e[33264];else if(i=e[s+1840>>2],e[s+1820>>2]=i,!((0|(a=e[33264]))<=0))for(o=0;l[0|i]==32&&(g[0|i]=45,o=o+1|0,i=e[s+1820>>2],a=e[33264]),i=i+1|0,e[s+1820>>2]=i,(0|a)>(0|o););o:if(!(a|(0|ee)!=1)&&(c=Gt(s+576|0,I=e[s+1840>>2]),l[c+I|0]==32)){o=s+1408|0,a=I;u:{l:{n:{for(;;){p:if(rr(e[s+576>>2])){C:{if(l[1+(f=a+c|0)|0]==46){L=0;m:switch(l[(i=c+2|0)+a|0]-32|0){case 0:break C;case 7:break m;default:break p}if(L=1,c=i,l[f+3|0]==115)break C;break p}if(L=1,(0|p)<=0)break n}if(!((0|c)<=0)){if(Z=3&c,m=0,c>>>0<4)i=0;else for(ve=-4&c,i=0,f=0;g[0|o]=l[a+i|0],g[o+1|0]=l[(1|i)+a|0],g[o+2|0]=l[(2|i)+a|0],g[o+3|0]=l[(3|i)+a|0],i=i+4|0,o=o+4|0,(0|ve)!=(0|(f=f+4|0)););if(Z)for(;g[0|o]=l[a+i|0],i=i+1|0,o=o+1|0,(0|Z)!=(0|(m=m+1|0)););}if(p=p+1|0,L)a=a+c|0;else if(c=Gt(s+576|0,a=3+(a+c|0)|0),l[a+c|0]==32)continue}break}if(!((0|p)<2)){(c=(c=o-(i=s+1408|0)|0)+(i=wt(I,i,c))|0)>>>0<a>>>0&&jA(c,32,(s+1408|0)+a-(i+o)|0),e[33264]=(p<<1)-2,e[s+1836>>2]=0;break l}}if(!p)break o;if(e[s+1832>>2]=0,e[s+1836>>2]=0,!e[33264])break u}e[s+1832>>2]=128}he=1}if(l[s+1616|0]==21){qA(189088,s+1616|0),i=0;break e}if(zA=l[s+1833|0],o=1,!ce){if(e[s+168>>2]-48>>>0<10){if(RA(A,84174,189088),i=0,l[189088]==21)break e;if(!(!(128&l[A+109|0])|32&l[r+2|0])){g[189088]=21,g[189089]=0;break e}o=!!(0|da(A,e[s+1840>>2],s+1616|0,s+1832|0,r,0))}else o=0;if(!(o|(3&E)==2)&&(16777216&(a=e[A+104>>2])||(o=0,!(!(33554432&a)|!(1&E))))&&(16&E||(o=0,!(1&g[r+13|0])))){ve=e[s+1840>>2],i=0,p=0,f=0,J=Z=J-224|0,g[0|(m=s+1616|0)]=0,e[Z+216>>2]=0,e[Z+220>>2]=0;o:if(!(g[ve-2|0]-48>>>0<10|(1&g[0|r]?0:2&l[A+107|0])||(a=l[0|(c=ve+1|0)],(!(2561&M[A+106>>1])|!(1&g[r+2|0]))&(0|a)==32))){if((0|(p=l[0|ve]))!=32){for(L=32767,a=0;;){if(!(i=ri(101868,p<<24>>24,8))){p=0;break o}if(I=0,(0|(i=e[(i<<2)-305584>>2]))==(0|a)&&!((0|(I=f+1|0))<=2)){p=0;break o}u:{l:{n:{p:{if(!((0|a)<2)){if((0|a)==10|(0|a)==100)break p;if(!((0|a)>(0|i))){p=0;break o}}if(!a)break l;if((0|a)<(0|i))break n;break l}if((0|a)>=(0|i))break l}if(p=0,(0|We)%10|(0|z(a,10))<(0|i))break o;i=i-a|0,L=a;break u}if((0|i)>=(0|L)){p=0;break o}We=a+We|0}if(p=l[0|c],c=o=c+1|0,a=i,f=I,(0|p)==32)break}a=l[0|o]}else o=c;if((a<<24>>24)-48>>>0<10)p=0;else if((0|(a=i+We|0))<e[A+120>>2])p=0;else if((0|a)>e[A+116>>2])p=0;else if(RA(A,85600,Z+176|0),i=m,4&l[A+107|0]||(i=qA(m,i=Z+176|0)+HA(i)|0),e[Z+4>>2]=e[A+140>>2],e[Z>>2]=a,DA(Z+16|0,85839,Z),p=0,l[0|o]!=46){fa(A,ve,o,r,1)&&(e[r>>2]=32768|e[r>>2]),c=0;u:if(8&l[A+107|0]){if(f=e[r>>2],e[A+212>>2]==26741){if(32768&f)break u;if(!(16384&f))break o;c=1,f=0;l:{n:switch(l[0|o]-97|0){case 0:case 4:break n;default:break l}n:{p:{C:{m:switch((L=l[o+1|0])-116|0){case 6:break l;case 1:case 2:case 3:case 4:case 5:break p;case 0:break m;default:break C}if(l[o+2|0]!=116)break n;break l}if((0|L)==32)break l}if(!((0|a)%1e3|0)&&(0|L)==108)break l}f=1}if(f)break u;break o}e[r>>2]=32768|f}e[(a=A+8232|0)>>2]=0,e[a+4>>2]=0,da(A,Z+16|2,i,Z+216|0,r,c),p=1,4&l[A+107|0]&&sa(m,Z+176|0)}}J=Z+224|0,p?(e[s+1832>>2]=8192|e[s+1832>>2],o=1):o=0}}if(L=ce?he:32&zA?1:he,he=0,!(!(1&E)|(0|ee)<2)&&is(e[s+168>>2])){o:{if(1&g[188785]){if(!(!(a=8192&(i=e[s+1832>>2]))|o))break o;he=a>>>2^2048;break c}if(o)break i;i=e[s+1832>>2]}if(!(128&i|ee>>>0>3)&&!((0|(i=e[A+8220>>2]))<4)&&(a=1,(0|i)>=e[A+8216>>2]))break A}}if(m=0,(0|L)<=0)break s;a=L;break A}if((0|(a=L))>0)break A;m=0,I=0,f=0,p=0;break r}if(o)I=0,f=0,p=0;else{a=e[s+1840>>2],e[s+1820>>2]=a,i=999,p=0,I=0,T=0;s:{i:{c:{for(;;){o:{u:{l:{if(i-1>>>0>=2){if((0|ee)<2||(Gt(s+1408|0,a),(0|(i=e[s+1408>>2]))<577&e[A+600>>2]>0)||(i=Hs(i),(e[i+4>>2]!=e[A+600>>2]?i:0)|e[A+40>>2]==1))break l;switch(i=l[0|a],e[s+1408>>2]=i<<24>>24,i-32|0){default:if(!i)break l;break;case 0:case 7:break l;case 1:case 2:case 3:case 4:case 5:case 6:}m=Gt(s+1408|0,a),ce=9;n:{p:{C:{m:if(-33&(i=e[s+1408>>2])){for(o=0,f=0;;){b:{h:{if((0|i)==39){if((0|I)>0|(0|o)>1)break m;if(f=o?f:39,e[A+40>>2]!=3)break h;break b}f=o?f:i}o=o+1|0}if(!d0(A,i)){if((0|(i=e[s+1408>>2]))!=39&&!is(i))break l;if(m=Gt(s+1408|0,a+m|0)+m|0,-33&(i=e[s+1408>>2]))continue;break m}break}if((0|o)<=2)break C;ce=o}else f=0;if((0|(i=e[A+40>>2]))!=2)break p;J=i=J-208|0,g[0|i]=0,o=l[0|(c=a-1|0)],g[0|c]=32,a=qt(A,a,i,200,0,-2147483648,0),g[0|c]=o,J=i+208|0,a=!a|(32768&a)>>>15;break n}i=e[A+40>>2],ce=o}a=(g[A+168|0]+1|0)<(ce-((0|i)==(0|f))|0)}if(!a)break l;a=e[s+1820>>2]}if(l[0|a]!=39)break u;p=67108864,he=0}if(m=0,rt(A,s+992|0,0,I),a=e[s+1820>>2],(0|(i=l[0|a]))!=32)break o;I=0,f=0;break r}if(he=0,a=zs(A,a,s+992|0,1&(T|=(0|I)>0))+e[s+1820>>2]|0,e[s+1820>>2]=a,l[s+992|0]==21)break c;for(I=I+1|0,o=0;o=(i=o)+1|0,l[a+i|0]!=32;);p=67108864;continue}break}if(!l[s+992|0]|(0|i)==39||(g[a-1|0]=32,a=e[s+1820>>2]),c=qt(A,a,s+1616|0,200,s+784|0,E,s+1832|0),(0|(a=l[s+1616|0]))==21){qA(189088,s+1616|0),i=0;break e}if(!(a|l[s+784|0])&&(Gt(s+1408|0,e[s+1820>>2]),(0|ee)==1&&(rr(e[s+1408>>2])||I0(e[s+1408>>2])))){kn(A,e[s+1820>>2],s+1616|0,L)&&qA(189088,s+1616|0),i=0;break e}e[s+172>>2]=g[e[s+1820>>2]-1|0];o:if(1024&c)for(ve=s+176|1,L=0,i=1,T=0,I=0;;){if((ce=131072&c)|!(1&i)||(g[s+1408|0]=0,!(a=qt(A,e[s+1820>>2],s+1408|0,200,s+576|0,805306368|E,s+1832|0)))){2048&c&&(e[A+8184>>2]=1),g[e[s+1820>>2]-1|0]=e[s+172>>2];u:{l:{n:{p:if(ce){if(g[s+176|0]=0,i=e[s+1820>>2],a=1,f=63&c){if(Z=1&c,I=f-1|0,o=0,(0|f)!=1)for(We=f-Z|0,f=0;m=i,e[s+1820>>2]=i+1,g[0|(Qe=(s+176|0)+a|0)]=(0|o)!=(0|I)?l[0|i]:0,i=i+2|0,e[s+1820>>2]=i,g[Qe+1|0]=(0|I)!=(1|o)?l[m+1|0]:0,o=o+2|0,a=a+2|0,(0|We)!=(0|(f=f+2|0)););Z&&(f=i+1|0,e[s+1820>>2]=f,g[(s+176|0)+a|0]=(0|o)!=(0|I)?l[0|i]:0,a=a+1|0,i=f)}g[(s+176|0)+a|0]=0}else{if(i=e[s+1820>>2],!(f=15&c))break n;if(a=0,o=f,m=3&c)for(;i=i+1|0,e[s+1820>>2]=i,(192&l[0|i])==128||(o=o-1|0,(0|m)!=(0|(a=a+1|0))););if(f>>>0<4)break p;for(;;)if(i=i+1|0,e[s+1820>>2]=i,(192&l[0|i])!=128){for(;i=i+1|0,e[s+1820>>2]=i,(192&l[0|i])==128;);for(;i=i+1|0,e[s+1820>>2]=i,(192&l[0|i])==128;);for(;i=i+1|0,e[s+1820>>2]=i,(192&l[0|i])==128;);if(a=(0|o)>4,o=o-4|0,!a)break}}if(a=i-1|0,e[s+172>>2]=g[0|a],g[0|a]=32,a=E|=8388608,!ce)break l;if(ar(s+576|0,s+784|0,12),e[s+1852>>2]=ve,a=qA(s+1200|0,i=s+1616|0),kr(A,s+1852|0,i,s+1832|0,0,r)&&qA(a,s+1616|0),!(32&l[s+1833|0]))break u;g[0|a]=0,kn(A,e[s+1852>>2],a,1);break u}a=i-1|0,e[s+172>>2]=g[0|a],g[0|a]=32,a=8388608|E}E=a,sa(s+1200|0,s+784|0)}if(g[s+784|0]=0,f=1,a=kr(A,s+1820|0,s+1616|0,s+1824|0,1024,r),e[s+1832>>2]||(i=e[s+1828>>2],e[s+1832>>2]=e[s+1824>>2],e[s+1836>>2]=i,f=T),a){m=0,I=c;break s}if(m=qt(A,e[s+1820>>2],s+1616|0,200,s+784|0,8404992&E,s+1832|0),i=1,I=c,T=f,l[s+1616|0]==21){g[e[s+1820>>2]-1|0]=e[s+172>>2],qA(189088,s+1616|0),i=0;break e}}else i=s+416|0,Fs(A,e[s+1820>>2],a,i),m=qt(A,e[s+1820>>2],s+1616|0,200,s+784|0,268435456|E,s+1832|0),wt(e[s+1820>>2],i,HA(i)),1024&m||(qA(s+1616|0,s+1408|0),i=qA(s+784|0,s+576|0),8&l[188788]&&(JA(f=i,i=s+576|0),c=e[47195],e[s+48>>2]=i,fr(c,85205,s+48|0)),m=a),i=0;if(a=(o=1024&m)>>>10|0,L>>>0>48)break o;if(L=L+1|0,c=m,!o)break}else m=c,I=0,T=0,a=0;if(a|!m)break i;for(c=qA(s+1408|0,s+1616|0),o=Fs(A,e[s+1820>>2],m,s+416|0),f=T,i=m;;){o:{if(g[s+1616|0]=0,l[s+1200|0]){if(g[e[s+1820>>2]-1|0]=e[s+172>>2],a=kr(A,s+1840|0,s+1616|0,s+1824|0,o,r),g[e[s+1820>>2]-1|0]=32,l[s+1616|0]==21){A=s+416|0,wt(e[s+1820>>2],A,HA(A)),qA(189088,s+1616|0),i=0;break e}if(e[s+1832>>2]||(T=e[s+1828>>2],e[s+1832>>2]=e[s+1824>>2],e[s+1836>>2]=T),a){g[s+1200|0]=0;break o}f=e[s+1824>>2]?1:f}if(a=kr(A,s+1820|0,s+1616|0,s+1824|0,o,r),l[s+1616|0]==21){A=s+416|0,wt(e[s+1820>>2],A,HA(A)),qA(189088,s+1616|0),i=0;break e}if(e[s+1832>>2]||(T=e[s+1828>>2],e[s+1832>>2]=e[s+1824>>2],e[s+1836>>2]=T),!a)if(16384&i)qA(s+1616|0,c);else{E|=o<<11&8192|i<<9&134217728;u:if(524288&i){if(T=qA(s+576|0,a=s+784|0),i=qt(A,e[s+1820>>2],s+1616|0,200,a,E,s+1832|0),sa(a,T),a=0,!i){i=0;break u}if(1024&i)break u;a=1,o=Fs(A,e[s+1820>>2],i,0)}else i=0,qt(A,e[s+1820>>2],s+1616|0,200,0,E,s+1832|0),a=0;if(l[s+1616|0]==21){qA(189088,s+1616|0),A=s+416|0,wt(e[s+1820>>2],A,HA(A)),g[e[s+1820>>2]-1|0]=e[s+172>>2],i=0;break e}if(a)continue}}break}65536&m||(dn(A,s+1616|0,200,s+784|0),g[s+784|0]=0),a=s+416|0,wt(e[s+1820>>2],a,HA(a));break s}qA(189088,A=s+992|0),i=!xa(1|A,84744,3)<<12;break e}m=0,f=T}g[e[s+1820>>2]-1|0]=e[s+172>>2]}}if(a=e[s+164>>2],e[s+1852>>2]=8026656,e[s+1848>>2]=8022304,e[s+1844>>2]=7566112,4&E){r:{if((i=255&a)&&((0|i)==102||(Ke=cA,Yn(a<<24>>24))))break r;Ke=MA}qt(A,Ke,189088,200,0,0,0)}for(a=0,i=s+1200|0,T=l[s+784|0];;){r:{s:{i:switch(0|(c=l[0|i])){case 0:break r;case 6:case 7:break i;default:break s}a=c}i=i+1|0;continue}break}r:if(a|f){if(e[A+32>>2]|65536&I){for(o=0,$t(A,s+1616|0,s+1832|0,3,0),i=s+1200|0;;){switch(l[0|i]){case 6:o&&(g[0|i]=5),o=1;default:i=i+1|0;continue;case 0:}break}e[s+24>>2]=s+1616,e[s+20>>2]=s+1200,e[s+16>>2]=s+992,as(189088,200,85233,s+16|0),g[189287]=0,$t(A,189088,s+1832|0,-1,0);break r}e[s+8>>2]=s+1616,e[s+4>>2]=s+1200,e[s>>2]=s+992,as(189088,200,85233,s),g[189287]=0,$t(A,189088,s+1832|0,-1,0)}else $t(A,a=s+1616|0,s+1832|0,-1,!!(0|T)<<1),e[s+40>>2]=a,e[s+36>>2]=s+1200,e[s+32>>2]=s+992,as(189088,200,85233,s+32|0),g[189287]=0;l[s+784|0]&&(a=HA(189088),g[983+(s-a|0)|0]=0,qA(a+189088|0,s+784|0)),16&(a=E|he)&&(e[s+1832>>2]=-268435457&e[s+1832>>2]);r:if(!(128&a)|!(16&l[A+14|0]))if(3072&a){if($e(A,6),!(2048&a))break r;e[s+1832>>2]=268435456|e[s+1832>>2]}else 16&l[z(e[33264],12)+r|0]&&(1536&(r=e[s+1832>>2])?$e(A,4):2048&r&&$e(A,3));else $e(A,3);8192&m&&(e[A+8192>>2]=2,e[A+8184>>2]=2);r:{if(8&(r=e[s+1836>>2]))e[A+8184>>2]=0,e[A+8188>>2]=3,a=A+8196|0;else if(1&r)e[A+8192>>2]=0,e[A+8184>>2]=2,a=A+8196|0;else if(2&r)e[A+8192>>2]=2,e[A+8184>>2]=0,e[A+8188>>2]=0,a=A+8196|0;else{if(!(4&r))break r;e[A+8184>>2]=0,e[A+8192>>2]=0,e[A+8196>>2]=2,a=A+8188|0}e[a>>2]=0}!l[e[s+1820>>2]]|256&r||((0|(r=e[A+8184>>2]))>0&&(e[A+8184>>2]=r-1),(0|(r=e[A+8192>>2]))>0&&(e[A+8192>>2]=r-1),(0|(r=e[A+8196>>2]))>0&&(e[A+8196>>2]=r-1),(0|(r=e[A+8188>>2]))<=0||(e[A+8188>>2]=r-1)),(0|ee)!=1|e[A+212>>2]!=25966||!is(e[s+168>>2])|e[s+168>>2]==105||(e[s+1832>>2]=16777216|e[s+1832>>2]);r:if(2&l[A+68|0]&&98304&(a=e[s+1832>>2])&&!((0|(r=HA(189088)-1|0))<=0))for(i=0;;){if(A=i+1|0,l[i+189088|0]==6){r=g[0|(A=A+189088|0)];s:{if(65536&a){if((0|ra(69))==(0|r)?(ee=ra(101),g[0|A]=ee):ee=l[0|A],o=111,(0|ra(79))==ee<<24>>24)break s;break r}if((0|ra(101))==(0|r)?(ee=ra(69),g[0|A]=ee):ee=l[0|A],o=79,(0|ra(111))!=ee<<24>>24)break r}g[0|A]=ra(o);break r}if((0|r)==(0|(i=A)))break}A=e[s+1832>>2],wt(t,s+256|0,rA),i=A|p;break e}if(i=0,g[s+1616|0]=0,kn(A,e[s+1840>>2],s+1616|0,a)){if(c=qA(189088,s+1616|0),!Qe){if(a=e[s+164>>2],e[s+1408>>2]=8026656,e[s+576>>2]=8022304,e[s+416>>2]=7566112,4&E){t=s+576|1,r=s+1408|1;A:{if((i=255&a)&&((0|i)==102||(T=t,Yn(a<<24>>24))))break A;T=r}qt(A,T,c,200,0,0,0)}i=128&e[s+1832>>2]}}else i=((0|ee)>1)<<12}else g[189088]=0;return J=s+1856|0,i}function Mt(A,t,r,a,s,i,o,c){var f,m,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0,MA=0,zA=0,et=0,at=0,dt=0,Ft=0,Zt=0,er=0,pr=0,hr=0,yr=0,Er=0,Fr=0,Wr=0,ia=0,Xr=0,na=0,Ba=0,es=0,ns=0,os=0,Ts=0,Gi=0,di=0,fi=0,Di=0;J=f=J-384|0,m=e[t>>2];e:{A:{r:{s:{if(s){if(l[0|s]!=7)break s;e[t>>2]=(a||1)+m;break r}e[i>>2]=0,e[t>>2]=m+1;break e}Ft=86135,Wr=268435456&o,ia=134217728&o,Xr=8388608&o,na=16384&o,Ba=8192&o,pr=r-1|0,hr=a-r|0,es=2&o,ns=128&o,os=o>>>31|0,Ts=-2147483648&o,yr=f+96|1;s:for(;;)for(e[f+268>>2]=0,ee=(E=e[t>>2])+a|0,cA=-2,Ke=-6,r=s,MA=Ts,et=0,Zt=0,rA=1,p=0,zA=0,dt=0;;){T=E,ce=p;i:{c:{o:{u:{l:{n:{p:{C:{m:{b:{h:{E:{w:{y:{L:{W:{for(;ve=r,r=r+1|0,!((I=l[0|ve])>>>0>9);)switch(L=r,0|I){case 0:if(!(r=Er)){Er=0,r=86135;break L}for(;;){I=1;V:{M:switch(0|(p=l[0|r])){case 0:case 3:break W;case 5:break M;default:break V}I=2}r=(r+I|0)+(((0|p)==9)<<1)|0}case 1:if(dt=1,!os)continue;break l;case 2:dt=2;continue;case 4:Er=r;continue;case 5:r=ve+2|0,p=e[A+320>>2];V:{if((E=l[ve+1|0])>>>0>=32){if(!(p>>>E-32&1))break V;break l}if(!(p>>>E&1))break l}rA=rA+1|0;continue;case 9:r=ve+3|0;continue;case 8:dt=1,Zt=1,MA=0;break;case 3:break y}L=0,p=ce,E=T;V:switch(0|dt){case 0:M:{B:{if((0|(p=l[0|ee]))!=(0|I)){if((0|p)!=69)break l;if((0|I)==101)break B;break l}if(L=0,(192&I)==128)break M}L=21}ee=ee+1|0,et=et+1|0;break i;case 1:break w;case 2:break V;default:break b}if(e[f+264>>2]=e[f+268>>2],!l[ee-1|0])break l;Ke=(0|(p=Ke+6|0))>=19?19:p,We=ee+1|0,Qe=Gt(f+268|0,ee),he=l[0|ee],L=20,p=ce;V:{M:switch(I-11|0){case 6:r=ve+2|0,p=e[f+268>>2],E=g[ve+1|0];B:if(I=e[604+(((E=((0|E)<65?191:-65)+E|0)<<2)+A|0)>>2])p=!!(0|Lr(I,p));else{if((0|E)>7)break l;D:{if((0|(I=e[A+600>>2]))>0){if((p=p-I|0)-1>>>0<255)break D;break l}if((I=p-192|0)>>>0<=413){p=l[344+(l[I+94240|0]+A|0)|0]&1<<E;break B}if(p>>>0>255)break l}p=l[344+(A+p|0)|0]&1<<E}if(!p)break l;ee=ee+Qe|0,L=((0|E)==2?19:20)-Ke|0;break i;case 7:if(r=ve+2|0,p=g[ve+1|0],!(p=e[4788+((((0|p)<65?191:-65)+p<<2)+A|0)>>2]))break l;B:for(;;){if((0|(L=l[0|p]))==7)break l;if((0|L)==126){L=20-Ke|0;break i}D:if(he){if(Z=ee,I=p,(0|L)==(0|he))for(;;){if((0|(L=l[0|(I=I+1|0)]))!=(0|(p=l[0|(Z=Z+1|0)])))break D;if(!p)break}}else I=p,Z=ee;if(L){for(;;)if(E=l[0|I],I=p=I+1|0,!E)continue B}break}if((0|(p=Z-ee|0))<0)break l;ee=p+ee|0,L=20-Ke|0;break i;case 14:I=e[f+268>>2];B:{D:if(p=e[A+604>>2])p=!!(0|Lr(p,I));else{F:{if((0|(p=e[A+600>>2]))>0){if((Z=I-p|0)-1>>>0<255)break F;break B}if((p=I-192|0)>>>0<=413){p=1&g[344+(l[p+94240|0]+A|0)|0];break D}if(Z=I,I>>>0>255)break c}p=1&g[344+(A+Z|0)|0]}if(p)break l;I=e[f+268>>2]}if(!ia)break c;if((0|I)==32)break l;break c;case 4:if((p=e[f+268>>2])-48>>>0<10|p-2406>>>0<10)break c;if(!l[A+170|0])break l;L=20-Ke|0;break i;case 5:if(vi(e[f+268>>2]))break l;break o;case 0:if(e[f+268>>2]==e[f+264>>2])break o;break l;case 17:r=ve+2|0,p=32768,L=0;B:{D:switch((I=l[ve+1|0])-1|0){case 0:break b;case 1:break D;default:break B}if(L=1,p=ce,!Xr)break b;break l}if((0|(p=240&I))==16){if(L=23,p=ce,c>>>(15&I)&16384)break b;break l}if((0|I)!=3&(0|p)!=32||(wt(p=f+96|0,pr,E=1+(e[t>>2]+(et+hr|0)|0)|0),g[0|(p=p+E|0)]=32,g[p+1|0]=0,e[33265]=0,e[33266]=0,e[f+16>>2]=yr,kr(A,f+16|0,f+272|0,133060,0,0),L=23,!((0|I)!=3|(0|(Z=e[33265]))>=0|16384&e[33266])))break i;if(p=ce,E=T,Z>>>(15&I)&16384)break b;break l;case 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y}p=l[ce+2|0],Z=l[0|s],e[ee+36>>2]=127&l[ce+3|0],e[ee+32>>2]=4&p?80:83,DA(ee+48|0,85131,ee+32|0),1&p&&(s=HA(s=ee+48|0)+s|0,g[0|s]=101,g[s+1|0]=0),2&(s=127&p)&&(p=HA(p=ee+48|0)+p|0,g[0|p]=105,g[p+1|0]=0),4&s&&(p=HA(p=ee+48|0)+p|0,g[0|p]=112,g[p+1|0]=0),8&s&&(p=HA(p=ee+48|0)+p|0,g[0|p]=118,g[p+1|0]=0),16&s&&(p=HA(p=ee+48|0)+p|0,g[0|p]=100,g[p+1|0]=0),32&s&&(p=HA(p=ee+48|0)+p|0,g[0|p]=102,g[p+1|0]=0),s>>>0>=64&&(s=HA(s=ee+48|0)+s|0,g[0|s]=113,g[s+1|0]=0),1&Z&&(s=HA(s=ee+48|0)+s|0,g[0|s]=116,g[s+1|0]=0),ce=ce+4|0,T=qA(T,s=ee+48|0)+HA(s)|0,Z=32;break y}ce=ce+2|0,Z=l[g[0|s]+93871|0];break y}s=g[0|s],g[0|T]=76,p=((s=s+((0|s)<65?191:-65)|0)>>>0)/10|0,g[T+1|0]=p+48,Z=s-z(p,10)|48,(0|zA)==1&&(g[0|T]=Z,Z=76),ce=ce+2|0,T=T+2|0;break y}Z=p>>>0<=31?l[p+93904|0]:(0|p)==32?95:Z,ce=s}if(g[0|T]=Z,s=ce+1|0,T=T+1|0,!((Z=l[0|ce])>>>0>=10))break}p=1;y:switch(0|Z){case 1:p=r;case 8:g[0|T]=0,T=ee+80|0,r=p,zA=1;continue;case 2:g[0|T]=0,ce=HA(p=ee+288|0)+p|0,T=l[84899]|l[84900]<<8,g[0|ce]=T,g[ce+1|0]=T>>>8,zA=2,g[ce+2|0]=l[84901],T=HA(p)+p|0;continue;case 5:he=g[0|s],s=ce+2|0;continue;case 9:MA=(l[0|s]+z(l[ce+2|0],255)|0)-256|0,s=ce+3|0;continue;case 0:case 3:break y;default:continue}break}if(g[0|T]=0,T=E,(0|MA)>0&&(e[ee+16>>2]=MA,DA(E,85581,ee+16|0),T=E+7|0),(0|he)>0&&(e[ee>>2]=he,DA(T,85694,ee),T=HA(T)+T|0),1&((0|(s=HA(ee+80|0)))>0|r)){1&r&&(g[0|T]=95,T=T+1|0);y:if(!((0|(ce=s-1|0))<0|T>>>0>=E>>>0))for(;;){if(g[0|T]=l[(ee+80|0)+ce|0],T=T+1|0,(0|ce)<=0)break y;if(ce=ce-1|0,!(T>>>0<E>>>0))break}g[0|T]=41,g[T+1|0]=32,T=T+2|0}g[0|T]=0,g[(r=ee+288|0)+((E+3|0)-T|0)|0]=0,sa(T,r),(0|(r=HA(E)))<=7&&(jA(r+E|0,32,8-r|0),r=8),g[r+E|0]=0,J=ee+496|0,e[f+4>>2]=E,e[f>>2]=(0|a)>1?I+35|0:I,e[f+8>>2]=ve,fr(We,89088,f)}r=L;break l}if(!l[0|T])break l;cA=(0|(p=cA+2|0))>=19?19:p,Gt(f+264|0,T),p=Ma(f+268|0,E=T-1|0),he=l[0|E],We=E;w:{y:{L:{W:{V:{M:switch(I-10|0){case 13:if(p=(0|(at=l[0|r]))==(0|(I=l[0|T])),Qe=-1,(0|I)==32|(0|I)==(0|at))break W;if(I)break V;break L;case 7:r=ve+2|0,L=e[f+268>>2],T=g[ve+1|0];B:if(I=e[604+(((T=((0|T)<65?191:-65)+T|0)<<2)+A|0)>>2])I=!!(0|Lr(I,L));else{if((0|T)>7)break l;D:{if((0|(I=e[A+600>>2]))>0){if((L=L-I|0)-1>>>0<255)break D;break l}if((I=L-192|0)>>>0<=413){I=l[344+(l[I+94240|0]+A|0)|0]&1<<T;break B}if(L>>>0>255)break l}I=l[344+(A+L|0)|0]&1<<T}if(!I)break l;E=1+(E-p|0)|0,L=((0|T)==2?19:20)-cA|0;break w;case 8:if(r=ve+2|0,p=g[ve+1|0],!(p=e[4788+((((0|p)<65?191:-65)+p<<2)+A|0)>>2]))break l;B:{for(;;){if((0|(he=l[0|p]))==7)break l;if((0|he)==126){Qe=0;break B}I=E;D:{F:{if((0|(ve=(Qe=HA(p))-1|0))>0)for(I=T-Qe|0,L=0,Z=E;;){if(!l[0|(Z=Z-1|0)])break F;if((0|ve)==(0|(L=L+1|0)))break}q:if(!((0|(L=l[0|I]))!=(0|he)|!L))for(;;){if((0|(he=l[0|(p=p+1|0)]))!=(0|(L=l[0|(I=I+1|0)])))break q;if(!L)break}if(!he)break D}for(I=p;L=l[0|I],I=p=I+1|0,L;);continue}break}if((0|Qe)<0)break l}L=20-Ke|0,E=1+(E-Qe|0)|0;break w;case 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19:if(L=3,(0|(Z=e[f+268>>2]))==32)break w;for(I=1+(E-p|0)|0;;){B:{D:if(p=e[A+632>>2])p=!!(0|Lr(p,Z));else{F:{if((0|(p=e[A+600>>2]))>0){if((Z=Z-p|0)-1>>>0<255)break F;break B}if((p=Z-192|0)>>>0<=413){p=128&l[344+(l[p+94240|0]+A|0)|0];break D}if(Z>>>0>255)break B}p=128&l[344+(A+Z|0)|0]}if(p)break l}if(I=I-Ma(f+268|0,I-1|0)|0,(0|(Z=e[f+268>>2]))==32)break}break w;case 16:if(L=1,p=ce,E=T,e[A+8184>>2])break b;break l;case 9:if(L=1,p=ce,E=T,es)break b;break l;case 36:for(;;){if(L=50,p=ce,!(T=(255&he)-32|0))break l;if((0|T)==14)break b;he=l[0|(We=We-1|0)]}case 35:break M;default:break y}M:{if(p=he-32|0){if((0|p)==13)break M;break l}if(!ns)break l}L=22-Ke|0;break w}V:{for(;;){Qe=-1,Z=T,T=T-1|0;M:if((0|at)==18&&(p=g[ve+2|0],L=e[4788+((((0|p)<65?191:-65)+p<<2)+A|0)>>2]))for(Di=Z+1|0;;){if((0|(We=l[0|L]))==7){Qe=-1;break M}if((0|We)==126){Qe=0;break M}I=Z;B:{if((0|(Fr=(Qe=HA(L))-1|0))>0)for(I=Di-Qe|0,he=0,p=Z;;){if(!l[0|(p=p-1|0)])break 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r,a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0,MA=0,zA=0,et=0,at=0,dt=0,Ft=0,Zt=0,er=0;J=r=J-1168|0,e[r+928>>2]=0,e[r+932>>2]=0,e[r+920>>2]=0,e[r+924>>2]=0,e[r+912>>2]=0,e[r+916>>2]=0,e[r+904>>2]=0,e[r+908>>2]=0,e[r+896>>2]=0,e[r+900>>2]=0;e:{A:{if(A){if(l[0|A]|8&t)break A;break e}if(!(8&t))break e}if(ar(r+1088|0,A,40),16&t){if((0|ss(qA(r+704|0,A)))<=0)break e;Z=8&t}else(Z=8&t)|l[r+1088|0]||(b[r+1088>>1]=l[85055]|l[85056]<<8,g[r+1090|0]=l[85057]),e[r+496>>2]=137584,e[r+500>>2]=47,e[r+504>>2]=47,DA(a=r+512|0,85286,r+496|0),e[r+484>>2]=r+1088,e[r+480>>2]=a,DA(a=r+704|0,85425,r+480|0),(0|ss(a))>0||(e[r+468>>2]=47,e[r+472>>2]=47,e[r+464>>2]=137584,DA(a=r+512|0,85648,r+464|0),e[r+452>>2]=r+1088,e[r+448>>2]=a,DA(r+704|0,85425,r+448|0));if(s=Z?86012:85055,!(ee=wa(r+704|0,85712))){if(a=0,3&t)break e;s=(0|wi(a=r+1088|0))<0?s:a}if((L=2&t)||(a=e[47192])&&(y0(a),e[47192]=0),ve=qA(r+992|0,s),he=qA(r+944|0,s),L?((a=$a(200992,43))&&(g[0|a]=0),e[r+432>>2]=A+3,DA(A=r+704|0,86030,r+432|0),sa(200992,A)):(e[32972]=199592,ar(200992,A,40),g[201088]=0,g[201040]=0,e[50299]=200992,e[50298]=201088,e[50297]=201040),Kt(L),ee){for(We=e[30450],Ke=r+548|0,rA=r+544|0,cA=r+540|0,MA=r+536|0,zA=r+532|0,et=r+528|0,at=12|(A=r+512|0),dt=8|A,Ft=4|A;Jt(r+704|0,190,ee);){A=r+704|0;A:{if(l[r+704|0]!=35){r:if(!((0|(A=HA(r+704|0)-1|0))<=0))for(;;){if(!((0|(s=g[0|(a=(r+704|0)+A|0)]))==32|s-9>>>0<5))break r;if(g[0|a]=0,!((0|(A=A-1|0))>0))break}if(!(A=Hr(r+704|0)))break A}g[0|A]=0}A=r+704|0;A:if(a=l[r+704|0])for(;;){if((0|(a=a<<24>>24))==32|a-9>>>0<5)break A;if(!(a=l[0|(A=A+1|0)]))break}if(g[0|A]=0,l[r+704|0])if(A=A+1|0,a=Cs(129744,r+704|0)){i=0,J=s=J-416|0;A:if(o=e[47192]){switch(a-19|0){case 16:if(e[s+32>>2]=s+412,(0|ut(A,84249,s+32|0))!=1)break A;e[o+324>>2]=e[s+412>>2];break A;case 8:j0(A,o+320|0,27);break A;case 2:if(e[s+48>>2]=188784,ut(A,84249,s+48|0),!(A=l[188784]))break A;e[o+152>>2]=A;break A;case 11:if(l[0|A]){for(c=e[30450];;)if(a=A,A=A+1|0,!((0|(i=g[0|a]))==32|i-9>>>0<5)){for(i=Vs(a),e[s+412>>2]=i,(0|i)>0&&(i>>>0<=31?e[o+104>>2]=e[o+104>>2]|1<<i:i>>>0<=63?e[o+108>>2]=e[o+108>>2]|1<<i-32:(e[s+64>>2]=i,fr(c,84700,s- -64|0)),a=A);a=(A=a)+1|0,(i=g[0|A])-48>>>0<10|(32|i)-97>>>0<26;);if(!i)break}}8&(A=e[o+104>>2])&&(e[o+124>>2]=46,e[o+128>>2]=44),4&A&&(e[o+124>>2]=0);break A;default:if((65280&a)!=256)break A;e[s+16>>2]=24+(o+((255&a)<<2)|0),ut(A,84249,s+16|0);break A;case 1:e[s+144>>2]=o,e[s+148>>2]=o+4,ut(A,85642,s+144|0);break A;case 3:if(a=0,jA(i=s+160|0,0,240),e[s+132>>2]=s+360,e[s+128>>2]=s+320,e[s+124>>2]=s+280,e[s+120>>2]=s+240,e[s+116>>2]=s+200,e[s+112>>2]=i,i=ut(A,85037,s+112|0),e[s+412>>2]=i,e[o+152>>2]=0,(0|i)<=0)break A;for(p=e[30450];;){r:if(vr(c=(s+160|0)+z(a,40)|0,85301)){s:{if((0|(m=e[34454]))>0)for(T=e[34455],A=0;;){if(!vr(c,T+z(A,68)|0))break s;if((0|m)==(0|(A=A+1|0)))break}e[s+96>>2]=c,fr(p,85562,s+96|0),i=e[s+412>>2];break r}g[156+(a+o|0)|0]=A}if(!((0|i)>(0|(a=a+1|0))))break}break A;case 9:e[s+88>>2]=o+20,e[s+84>>2]=o+16,e[s+80>>2]=o+8,ut(A,84778,s+80|0);break A;case 10:j0(A,o+12|0,29);break A;case 5:if((0|(p=Cn(A,s+160|0)))<=0)break A;if(a=0,A=0,p>>>0>=4)for(T=-4&p,c=o+304|0;m=s+160|0,b[c+(A<<1)>>1]=e[m+(A<<2)>>2],b[c+((f=1|A)<<1)>>1]=e[m+(f<<2)>>2],b[c+((f=2|A)<<1)>>1]=e[m+(f<<2)>>2],b[c+((f=3|A)<<1)>>1]=e[m+(f<<2)>>2],A=A+4|0,(0|T)!=(0|(i=i+4|0)););if(!(i=3&p))break A;for(;b[304+(o+(A<<1)|0)>>1]=e[(s+160|0)+(A<<2)>>2],A=A+1|0,(0|i)!=(0|(a=a+1|0)););break A;case 6:if((0|(p=Cn(A,s+160|0)))<=0)break A;if(a=0,A=0,p>>>0>=4)for(T=-4&p,c=o+296|0;m=s+160|0,g[A+c|0]=e[m+(A<<2)>>2],g[(f=1|A)+c|0]=e[m+(f<<2)>>2],g[(f=2|A)+c|0]=e[m+(f<<2)>>2],g[(f=3|A)+c|0]=e[m+(f<<2)>>2],A=A+4|0,(0|T)!=(0|(i=i+4|0)););if(!(i=3&p))break A;for(;g[296+(A+o|0)|0]=e[(s+160|0)+(A<<2)>>2],A=A+1|0,(0|i)!=(0|(a=a+1|0)););break A;case 7:if((0|(p=Cn(A,s+160|0)))<=0)break A;if(a=0,A=0,p>>>0>=4)for(T=-4&p,c=o+304|0;f=m=c+(A<<1)|0,I=M[m>>1],m=s+160|0,b[f>>1]=I+M[m+(A<<2)>>1],b[(I=c+((f=1|A)<<1)|0)>>1]=M[I>>1]+M[m+(f<<2)>>1],b[(I=c+((f=2|A)<<1)|0)>>1]=M[I>>1]+M[m+(f<<2)>>1],b[(I=c+((f=3|A)<<1)|0)>>1]=M[I>>1]+M[m+(f<<2)>>1],A=A+4|0,(0|T)!=(0|(i=i+4|0)););if(!(i=3&p))break A;for(;b[304+(c=o+(A<<1)|0)>>1]=M[c+304>>1]+M[(s+160|0)+(A<<2)>>1],A=A+1|0,(0|i)!=(0|(a=a+1|0)););break A;case 4:g[o+169|0]=1;break A;case 0:}g[o+208|0]=1}else e[s>>2]=xi(129568,a),fr(e[30450],89101,s);J=s+416|0}else{switch(Cs(131904,r+704|0)-1|0){case 1:if(L||(g[r+1040|0]=0,e[r+512>>2]=5,e[r+32>>2]=r+1040,e[r+36>>2]=r+512,ut(A,86237,r+32|0),e[r+1040>>2]==1769103734&e[r+1044>>2]==7630433))continue;if((A=HA(r+1040|0)+2|0)>>>0<99-ce>>>0&&(g[0|(a=ce+201088|0)]=e[r+512>>2],qA(a+1|0,r+1040|0),ce=A+ce|0),!Zt){if(A=0,(a=c=r+1040|0)||(a=e[57150])){if(A=86875,e[24+(s=J-32|0)>>2]=0,e[s+28>>2]=0,e[s+16>>2]=0,e[s+20>>2]=0,e[s+8>>2]=0,e[s+12>>2]=0,e[s>>2]=0,e[s+4>>2]=0,o=0,i=l[86875])if(l[86876]){for(;e[(o=s+(i>>>3&28)|0)>>2]=e[o>>2]|1<<i,i=l[A+1|0],A=A+1|0,i;);A:if(i=l[0|(A=a)])for(;;){if(!(e[s+(i>>>3&28)>>2]>>>i&1))break A;if(i=l[A+1|0],A=A+1|0,!i)break}o=A-a|0}else{for(A=a;s=A,A=A+1|0,l[0|s]==(0|i););o=s-a|0}if(l[0|(A=o+a|0)]){a=86875,J=i=J-32|0,s=g[86875];A:if(l[86876]&&s){if(jA(i,0,32),s=l[86875])for(;e[(o=i+(s>>>3&28)|0)>>2]=e[o>>2]|1<<s,s=l[a+1|0],a=a+1|0,s;);if(a=A,s=l[0|A])for(;;){if(e[i+(s>>>3&28)>>2]>>>s&1)break A;if(s=l[a+1|0],a=a+1|0,!s)break}}else a=Rs(A,s);J=i+32|0,l[0|(a=(a-A|0)+A|0)]?(e[57150]=a+1,g[0|a]=0):e[57150]=0}else e[57150]=0,A=0}a=qA(ve,A),qA(he,A),wi(qA(r+896|0,A)),e[47192]=qe(a),ar(e[32972]+40|0,c,20)}Zt=1;continue;case 0:if(L)continue;for(;a=A,A=A+1|0,(0|(s=g[0|a]))==32|s-9>>>0<5;);ar(201040,a,40);continue;case 2:e[r+1152>>2]=0,a=r+512|0,e[r+48>>2]=a,e[r+52>>2]=r+1152,ut(A,86237,r+48|0),g[201200]=Cs(132112,a),g[201201]=e[r+1152>>2];continue;case 4:e[r+64>>2]=he,ut(A,86939,r- -64|0);continue;case 3:e[r+80>>2]=r+896,ut(A,86939,r+80|0);continue;case 8:if(e[r+1152>>2]=100,e[r+1164>>2]=100,e[r+1148>>2]=100,e[r+112>>2]=r+1144,e[r+1144>>2]=0,e[r+96>>2]=r+512,e[r+100>>2]=r+1152,e[r+104>>2]=r+1164,e[r+108>>2]=r+1148,(0|ut(A,91156,r+96|0))<2||(A=e[r+512>>2])>>>0>8||((0|(a=e[r+1152>>2]))>=0&&(s=e[32972]+(A<<1)|0,a=Ne(E=2.56001*+(0|a))<2147483648?~~E:-2147483648,b[s+236>>1]=a,b[s+164>>1]=a),(0|(a=e[r+1164>>2]))>=0&&(s=e[32972]+(A<<1)|0,a=Ne(E=2.56001*+(0|a))<2147483648?~~E:-2147483648,b[s+254>>1]=a,b[s+182>>1]=a),(0|(s=e[r+1148>>2]))<0?a=e[32972]:(c=(a=e[32972])+(A<<1)|0,s=Ne(E=2.56001*+(0|s))<2147483648?~~E:-2147483648,b[c+200>>1]=s),b[218+((A<<1)+a|0)>>1]=e[r+1144>>2],A))continue;b[a+200>>1]=(0|z(b[a+200>>1],105))/100;continue;case 9:if(e[r+132>>2]=r+696,e[r+128>>2]=r+700,(0|ut(A,87106,r+128|0))!=2)continue;if(A=e[32972],a=e[r+700>>2],e[A+64>>2]=(a<<12)-36864,e[A+68>>2]=z(e[r+696>>2]-a|0,108),Ne(E=256*(+(a-82|0)/82*.25+1))<2147483648){e[A+116>>2]=~~E;continue}e[A+116>>2]=-2147483648;continue;case 35:er||wi(r+896|0),e[r+1164>>2]=0,g[r+1156|0]=l[91267],e[r+1152>>2]=l[91263]|l[91264]<<8|l[91265]<<16|l[91266]<<24,e[r+144>>2]=r+1164,e[r+148>>2]=r+512,e[r+152>>2]=r+1152,(0|ut(A,91302,r+144|0))<2|e[49848]>59||(A=bn(r+512|0))&&(g[z(e[49848],3)+199408|0]=A,A=bn(r+1152|0),a=e[49848],s=z(a,3)+199408|0,g[s+1|0]=A,e[49848]=a+1,g[s+2|0]=e[r+1164>>2]),er=1;continue;case 10:e[r+1140>>2]=0,a=e[32972],e[a+100>>2]=0,e[r+164>>2]=a+100,e[r+160>>2]=a+96,ut(A,87106,r+160|0);continue;case 11:if(e[r+176>>2]=r+1140,(0|ut(A,87268,r+176|0))!=1)continue;e[e[32972]+88>>2]=e[r+1140>>2]<<5;continue;case 12:if(e[r+192>>2]=r+1140,(0|ut(A,87268,r+192|0))!=1)continue;e[e[32972]+92>>2]=e[r+1140>>2];continue;case 13:if(e[r+208>>2]=r+1140,(0|ut(A,87268,r+208|0))!=1)continue;a=e[32972],(0|(A=e[r+1140>>2]))>=5&&(e[a+108>>2]=1,e[r+1140>>2]=4,A=4),e[a+104>>2]=A+1;continue;case 14:for(e[r+552>>2]=-1,e[r+556>>2]=-1,e[r+544>>2]=-1,e[r+548>>2]=-1,e[r+536>>2]=-1,e[r+540>>2]=-1,e[r+528>>2]=-1,e[r+532>>2]=-1,e[r+240>>2]=et,e[r+244>>2]=zA,e[r+248>>2]=MA,e[r+252>>2]=cA,e[r+256>>2]=rA,e[r+260>>2]=Ke,e[r+520>>2]=-1,e[r+524>>2]=-1,e[r+512>>2]=-1,e[r+516>>2]=-1,e[r+228>>2]=Ft,e[r+232>>2]=dt,e[r+236>>2]=at,e[r+224>>2]=r+512,ut(A,84222,r+224|0),T=e[32972],A=0,i=e[r+516>>2],s=0;;){if(a=s,o=i,c=A,(0|(s=e[(i=(A<<=2)+(r+512|0)|0)>>2]))==-1&&(s=8e3,e[i>>2]=8e3,c&&(e[(r+512|0)+(4|A)>>2]=e[508+(A+r|0)>>2])),i=e[(r+512|0)+(4|A)>>2],!((0|a)>=(0|(s=(0|s)/8|0))||(0|(m=s-a|0))<=0||(p=a+1|0,A=a,1&m&&(g[344+(a+T|0)|0]=(0|o)>=255?255:o,A=p),(0|s)==(0|p))))for(p=i-o|0;I=T+344|0,f=o+((0|z(p,A-a|0))/(0|m)|0)|0,g[I+A|0]=(0|f)>=255?255:f,f=o+((0|z(p,(Qe=A+1|0)-a|0))/(0|m)|0)|0,g[I+Qe|0]=(0|f)>=255?255:f,(0|s)!=(0|(A=A+2|0)););if(A=c+2|0,!(c>>>0<10))break}continue;case 15:if(e[r+272>>2]=r+1140,(0|ut(A,87268,r+272|0))!=1)continue;e[e[32972]+112>>2]=(e[r+1140>>2]<<6)/100;continue;case 16:a=e[32972],e[(s=a+300|0)>>2]=0,e[s+4>>2]=0,e[(i=a+292|0)>>2]=0,e[i+4>>2]=0,e[(o=a+284|0)>>2]=0,e[o+4>>2]=0,e[(c=a+276|0)>>2]=0,e[c+4>>2]=0,e[r+316>>2]=a+304,e[r+312>>2]=s,e[r+308>>2]=a+296,e[r+304>>2]=i,e[r+300>>2]=a+288,e[r+296>>2]=o,e[r+292>>2]=a+280,e[r+288>>2]=c,a=ut(A,84553,r+288|0),A=e[32972],e[A+272>>2]=a,e[A+276>>2]=0-e[A+276>>2],e[A+284>>2]=0-e[A+284>>2],e[A+292>>2]=0-e[A+292>>2],e[A+300>>2]=0-e[A+300>>2];continue;case 17:a=e[32972],e[(s=a+336|0)>>2]=0,e[s+4>>2]=0,e[(i=a+328|0)>>2]=0,e[i+4>>2]=0,e[(o=a+320|0)>>2]=0,e[o+4>>2]=0,e[(c=a+312|0)>>2]=0,e[c+4>>2]=0,e[r+348>>2]=a+340,e[r+344>>2]=s,e[r+340>>2]=a+332,e[r+336>>2]=i,e[r+332>>2]=a+324,e[r+328>>2]=o,e[r+324>>2]=a+316,e[r+320>>2]=c,A=ut(A,84553,r+320|0),e[e[32972]+308>>2]=A;continue;case 36:a=e[32972],e[r+352>>2]=a+120,e[r+356>>2]=a+124,e[r+1140>>2]=ut(A,87106,r+352|0);continue;case 33:e[r+368>>2]=e[32972]+84,ut(A,87268,r+368|0),Os(3);continue;case 31:a=e[32972],e[(s=a+156|0)>>2]=0,e[s+4>>2]=0,e[(i=a+148|0)>>2]=0,e[i+4>>2]=0,e[(o=a+140|0)>>2]=0,e[o+4>>2]=0,e[(c=a+132|0)>>2]=0,e[c+4>>2]=0,e[r+412>>2]=a+160,e[r+408>>2]=s,e[r+404>>2]=a+152,e[r+400>>2]=i,e[r+396>>2]=a+144,e[r+392>>2]=o,e[r+388>>2]=a+136,e[r+384>>2]=c,ut(A,84553,r+384|0),A=e[32972],e[A+152>>2]=e[A+152>>2]-40;continue;case 32:e[r+416>>2]=145740,ut(A,87268,r+416|0),Os(3);continue;case 6:case 7:continue}e[r+16>>2]=r+704,fr(We,87359,r+16|0)}}ta(ee)}A:{if((A=e[47192])|L){if(L)break A}else A=qe(ve),e[47192]=A;r:{if(!Z){if((0|(A=wi(r+896|0)))<0&&(e[r>>2]=r+896,fr(e[30450],87567,r),A=0),e[e[32972]+60>>2]=A,a=e[47192],e[a+292>>2]=A,Xa(a,he,4&t),l[132848])break r;y0(e[47192]),a=0;break e}e[e[32972]+60>>2]=0,e[A+292>>2]=0}g[ce+201088|0]=0}a=e[32972]}return J=r+1168|0,a}function ut(A,t,r){var a,s,i,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0;J=i=J-16|0,e[i+12>>2]=r,J=o=J-144|0,a=jA(o,0,144),e[a+76>>2]=-1,e[a+44>>2]=A,e[a+32>>2]=18,e[a+84>>2]=A,o=t,Ke=r,A=0,J=s=J-304|0;e:{A:{r:if(e[a+4>>2]||(Yi(a),e[a+4>>2])){if(!(t=l[0|o]))break e;s:{i:{c:{o:{for(;;){u:{l:if((0|(t&=255))==32|t-9>>>0<5){for(;t=o,o=o+1|0,(0|(r=l[t+1|0]))==32|r-9>>>0<5;);for(Ja(a,0,0);(0|(r=e[a+4>>2]))==e[a+104>>2]?r=bA(a):(e[a+4>>2]=r+1,r=l[0|r]),(0|r)==32|r-9>>>0<5;);o=e[a+4>>2],(0|(r=e[a+116>>2]))>0|(0|r)>=0&&(o=o-1|0,e[a+4>>2]=o),o=r=o-e[a+44>>2]|0,p=ce+e[a+124>>2]|0,p=(c=r>>31)+((r=L+e[a+120>>2]|0)>>>0<L>>>0?p+1|0:p)|0,ce=(L=r+o|0)>>>0<r>>>0?p+1|0:p}else{n:{p:{C:{if(l[0|o]==37){if((0|(t=l[o+1|0]))==42)break C;if((0|t)!=37)break p}if(Ja(a,0,0),l[0|o]!=37)(0|(t=e[a+4>>2]))==e[a+104>>2]?t=bA(a):(e[a+4>>2]=t+1,t=l[0|t]);else{for(;(0|(t=e[a+4>>2]))==e[a+104>>2]?t=bA(a):(e[a+4>>2]=t+1,t=l[0|t]),(0|t)==32|t-9>>>0<5;);o=o+1|0}if(l[0|o]!=(0|t)){if((0|(r=e[a+116>>2]))>0|(0|r)>=0&&(e[a+4>>2]=e[a+4>>2]-1),(0|t)>=0||(f=0,rA))break e;break r}m=(c=t=e[a+4>>2]-e[a+44>>2]|0)>>31,t=ce+e[a+124>>2]|0,f=((r=L+e[a+120>>2]|0)>>>0<L>>>0?t+1|0:t)+m|0,ce=(L=r+c|0)>>>0<r>>>0?f+1|0:f,t=o;break l}I=0,t=o+2|0;break n}l[o+2|0]!=36|t-48>>>0>=10?(I=e[Ke>>2],Ke=Ke+4|0,t=o+1|0):(t=l[o+1|0]-48|0,e[12+(r=J-16|0)>>2]=Ke,t=(t>>>0>1?(t<<2)-4|0:0)+Ke|0,e[r+8>>2]=t+4,I=e[t>>2],t=o+3|0)}if(E=0,o=0,l[0|t]-48>>>0<10)for(;o=(l[0|t]+z(o,10)|0)-48|0,r=l[t+1|0],t=t+1|0,r-48>>>0<10;);(0|(Z=l[0|t]))==109&&(T=0,E=!!(0|I),Z=l[t+1|0],A=0,t=t+1|0),t=(r=t)+1|0,c=3,f=E;n:{p:switch(Z-65|0){case 39:c=r+2|0,t=(r=l[r+1|0]==104)?c:t,c=r?-2:-1;break n;case 43:c=r+2|0,t=(r=l[r+1|0]==108)?c:t,c=r?3:1;break n;case 51:case 57:c=1;break n;case 11:c=2;break n;case 41:break n;case 0:case 2:case 4:case 5:case 6:case 18:case 23:case 26:case 32:case 34:case 35:case 36:case 37:case 38:case 40:case 45:case 46:case 47:case 50:case 52:case 55:break p;default:break s}c=0,t=r}f=c,Qe=(c=(47&(r=l[0|t]))==3)?1:f;n:if((0|(ve=c?32|r:r))!=91){p:{if((0|ve)!=110){if((0|ve)!=99)break p;o=(0|o)<=1?1:o;break n}k0(I,Qe,L,ce);break l}for(Ja(a,0,0);(0|(r=e[a+4>>2]))==e[a+104>>2]?r=bA(a):(e[a+4>>2]=r+1,r=l[0|r]),(0|r)==32|r-9>>>0<5;);r=e[a+4>>2],(0|(c=e[a+116>>2]))>0|(0|c)>=0&&(r=r-1|0,e[a+4>>2]=r),c=r=r-e[a+44>>2]|0,p=ce+e[a+124>>2]|0,ce=(f=r>>31)+((r=L+e[a+120>>2]|0)>>>0<L>>>0?p+1|0:p)|0,ce=(L=r+c|0)>>>0<r>>>0?ce+1|0:ce}if(ee=o,Ja(a,o,he=o>>31),(0|(r=e[a+4>>2]))==e[a+104>>2]){if((0|bA(a))<0)break i}else e[a+4>>2]=r+1;(0|(r=e[a+116>>2]))>0|(0|r)>=0&&(e[a+4>>2]=e[a+4>>2]-1),r=16;n:{p:{C:{m:{switch(ve-88|0){default:if((r=ve-65|0)>>>0>6|!(1<<r&113))break n;case 9:case 13:case 14:case 15:if(BA(s+8|0,a,Qe,0),r=e[a+4>>2]-e[a+44>>2]|0,e[a+120>>2]!=(0-r|0)|e[a+124>>2]!=(0-((r>>31)+!!(0|r)|0)|0))break C;break c;case 3:case 11:case 27:if((16|ve)==115){if(jA(s+32|0,-1,257),g[s+32|0]=0,(0|ve)!=115)break p;g[s+65|0]=0,g[s+46|0]=0,b[s+42>>1]=0,b[s+44>>1]=0;break p}jA(s+32|0,m=(0|(c=l[t+1|0]))==94,257),g[s+32|0]=0,r=m?t+2|0:t+1|0;b:{h:{E:{if((0|(t=l[(m?2:1)+t|0]))!=45){if((0|t)==93)break E;c=(0|c)!=94,t=r;break b}c=(0|c)!=94,g[s+78|0]=c;break h}c=(0|c)!=94,g[s+126|0]=c}t=r+1|0}for(;;){if((0|(r=l[0|t]))==45){if(r=45,!(!(m=l[t+1|0])|(0|m)==93)){if(f=t+1|0,m>>>0<=(t=l[t-1|0])>>>0)r=m;else for(;g[(t=t+1|0)+(s+32|0)|0]=c,(r=l[0|f])>>>0>t>>>0;);t=f}}else{if(!r)break i;if((0|r)==93)break p}g[33+(r+s|0)|0]=c,t=t+1|0}case 23:r=8;break m;case 12:case 29:r=10;break m;case 1:case 2:case 4:case 5:case 6:case 7:case 8:case 10:case 16:case 18:case 19:case 20:case 21:case 22:case 25:case 26:case 28:case 30:case 31:break n;case 0:case 24:case 32:break m;case 17:}r=0}m=0,p=0,c=0,f=0,Z=0,J=We=J-16|0;m:if((0|r)!=1&r>>>0<=36){for(;(0|(o=e[a+4>>2]))==e[a+104>>2]?o=bA(a):(e[a+4>>2]=o+1,o=l[0|o]),(0|o)==32|o-9>>>0<5;);b:{h:switch(o-43|0){case 0:case 2:break h;default:break b}Z=(0|o)==45?-1:0,(0|(o=e[a+4>>2]))==e[a+104>>2]?o=bA(a):(e[a+4>>2]=o+1,o=l[0|o])}b:{h:{E:{w:{if(!(!!(0|r)&(0|r)!=16|(0|o)!=48)){if((0|(o=e[a+4>>2]))==e[a+104>>2]?o=bA(a):(e[a+4>>2]=o+1,o=l[0|o]),(-33&o)==88){if(r=16,(0|(o=e[a+4>>2]))==e[a+104>>2]?o=bA(a):(e[a+4>>2]=o+1,o=l[0|o]),l[o+121329|0]<16)break E;(0|(r=e[a+116>>2]))>0|(0|r)>=0&&(e[a+4>>2]=e[a+4>>2]-1),Ja(a,0,0);break m}if(r)break w;r=8;break E}if(!((r=r||10)>>>0>l[o+121329|0])){(0|(r=e[a+116>>2]))>0|(0|r)>=0&&(e[a+4>>2]=e[a+4>>2]-1),Ja(a,0,0),e[56798]=28;break m}}if((0|r)==10){if((c=o-48|0)>>>0<=9){for(r=0;f=(r=z(r,10)+c|0)>>>0<429496729,(0|(o=e[a+4>>2]))==e[a+104>>2]?o=bA(a):(e[a+4>>2]=o+1,o=l[0|o]),f&(c=o-48|0)>>>0<=9;);m=r}w:if(!(c>>>0>9)){for(r=It(m,0,10,0),f=$;;){if(p=f,f=(0|(p=(m=r+c|0)>>>0<c>>>0?p+1|0:p))==429496729&m>>>0>=2576980378|p>>>0>429496729,(0|(r=e[a+4>>2]))==e[a+104>>2]?o=bA(a):(e[a+4>>2]=r+1,o=l[0|r]),f|(c=o-48|0)>>>0>9)break w;if(r=It(m,p,10,0),!((0|(f=$))==-1&~c>>>0>=r>>>0|(0|f)!=-1))break}r=10;break h}if(r=10,c>>>0<=9)break h;break b}}if(r-1&r){if((f=l[o+121329|0])>>>0<r>>>0){for(;m=(c=z(r,c)+f|0)>>>0<119304647,(0|(o=e[a+4>>2]))==e[a+104>>2]?o=bA(a):(e[a+4>>2]=o+1,o=l[0|o]),m&(f=l[o+121329|0])>>>0<r>>>0;);m=c}if(r>>>0<=f>>>0)break h;for(;;){if(c=It(m,p,r,0),(0|(ee=$))==-1&~(f&=255)>>>0<c>>>0||(p=ee,p=(m=c+f|0)>>>0<f>>>0?p+1|0:p,(0|(o=e[a+4>>2]))==e[a+104>>2]?o=bA(a):(e[a+4>>2]=o+1,o=l[0|o]),r>>>0<=(f=l[o+121329|0])>>>0))break h;if(Kr(We,r,0,0,0,m,p,0,0),e[We+8>>2]|e[We+12>>2])break}}else{if(ee=g[84400+(z(r,23)>>>5&7)|0],(c=l[o+121329|0])>>>0<r>>>0){for(;m=(f=f<<ee|c)>>>0<134217728,(0|(o=e[a+4>>2]))==e[a+104>>2]?o=bA(a):(e[a+4>>2]=o+1,o=l[0|o]),m&(c=l[o+121329|0])>>>0<r>>>0;);m=f}if(!(r>>>0<=c>>>0||(he=31&ee,(63&ee)>>>0>=32?(f=0,he=-1>>>he|0):he=(f=-1>>>he|0)|(1<<he)-1<<32-he,!f&m>>>0>he>>>0)))for(;;){if(cA=255&c,c=m,o=31&ee,(63&ee)>>>0>=32?(p=c<<o,o=0):(p=(1<<o)-1&c>>>32-o|p<<o,o=c<<o),m=cA|o,(0|(o=e[a+4>>2]))==e[a+104>>2]?o=bA(a):(e[a+4>>2]=o+1,o=l[0|o]),r>>>0<=(c=l[o+121329|0])>>>0)break h;if(!((0|f)==(0|p)&m>>>0<=he>>>0|f>>>0>p>>>0))break}}}if(!(l[o+121329|0]>=r>>>0)){for(;(0|(o=e[a+4>>2]))==e[a+104>>2]?o=bA(a):(e[a+4>>2]=o+1,o=l[0|o]),l[o+121329|0]<r>>>0;);e[56798]=68,Z=0,m=-1,p=-1}}(0|(r=e[a+116>>2]))>0|(0|r)>=0&&(e[a+4>>2]=e[a+4>>2]-1),m=(r=m^Z)-Z|0,p=((o=Z>>31)^p)-((r>>>0<Z>>>0)+o|0)|0}else e[56798]=28;if(J=We+16|0,r=e[a+4>>2]-e[a+44>>2]|0,e[a+120>>2]==(0-r|0)&e[a+124>>2]==(0-((r>>31)+!!(0|r)|0)|0))break c;if(!(!I|(0|ve)!=112)){e[I>>2]=m;break n}k0(I,Qe,m,p);break n}if(!I)break n;o=e[s+16>>2],r=e[s+20>>2],c=e[s+8>>2],E=e[s+12>>2];C:switch(0|Qe){case 0:J=p=J-32|0;m:if(0|(f=(m=2147483647&r)-1065418752|0)>>>0<(ee=m-1082064896|0)>>>0){if(m=(33554431&r)<<7|o>>>25,ee=f=0,!(!f&(0|(o&=33554431))==16777216?!(c|E):!f&o>>>0<16777216)){f=m+1073741825|0;break m}if(f=m+1073741824|0,16777216^o|c|E|ee)break m;f=(1&m)+f|0}else(!o&(0|m)==2147418112?!(c|E):m>>>0<2147418112)?(f=2139095040,m>>>0>1082064895||(f=0,(m=m>>>16|0)>>>0<16145||(ea(p+16|0,c,E,o,f=65535&r|65536,m-16129|0),Ya(p,c,E,o,f,16257-m|0),o=e[p+8>>2],f=(33554431&e[p+12>>2])<<7|o>>>25,m=e[p>>2]|!!(e[p+16>>2]|e[p+24>>2]|e[p+20>>2]|e[p+28>>2]),E=e[p+4>>2],(!(c=0)&(0|(o&=33554431))==16777216?!(m|E):!c&o>>>0<16777216)?16777216^o|m|c|E||(f=(1&f)+f|0):f=f+1|0))):f=4194303&((33554431&r)<<7|o>>>25)|2143289344;J=p+32|0,e[I>>2]=-2147483648&r|f;break n;case 1:D[I>>3]=ys(c,E,o,r);break n;case 2:break C;default:break n}e[I>>2]=c,e[I+4>>2]=E,e[I+8>>2]=o,e[I+12>>2]=r;break n}c=(We=(0|ve)!=99)?31:o+1|0;p:if((0|Qe)!=1){if(E){if(o=0,!(r=Ct(c)))break o;for(;;){for(A=r;;){if((0|(r=e[a+4>>2]))==e[a+104>>2]?r=bA(a):(e[a+4>>2]=r+1,r=l[0|r]),!l[33+(r+s|0)|0]){c=0,T=A;break p}if(g[A+o|0]=r,(0|c)==(0|(o=o+1|0)))break}if(f=1,!(r=Ur(A,c=c<<1|1)))break}T=A,A=0;break s}if(o=0,I)for(;;){if((0|(A=e[a+4>>2]))==e[a+104>>2]?A=bA(a):(e[a+4>>2]=A+1,A=l[0|A]),!l[33+(A+s|0)|0]){c=0,T=A=I;break p}g[o+I|0]=A,o=o+1|0}for(;(0|(A=e[a+4>>2]))==e[a+104>>2]?A=bA(a):(e[a+4>>2]=A+1,A=l[0|A]),l[33+(A+s|0)|0];);A=0,T=0,c=0}else{if(r=I,E&&!(r=Ct(c<<2)))break o;for(e[s+296>>2]=0,e[s+300>>2]=0,o=0;;){A=r;C:{for(;;){if((0|(r=e[a+4>>2]))==e[a+104>>2]?r=bA(a):(e[a+4>>2]=r+1,r=l[0|r]),!l[33+(r+s|0)|0])break C;g[s+27|0]=r,m=s+28|0,r=e[(f=(r=s+296|0)||228604)>>2];m:{b:{h:{E:{if(!(T=s+27|0)){if(r)break E;r=0;break m}if(!r){if((0|(p=(r=l[0|T])<<24>>24))>=0){m&&(e[m>>2]=r),r=!!(0|p);break m}if(!e[e[56841]>>2]){if(r=1,!m)break h;e[m>>2]=57343&p,r=1;break m}if((r=r-194|0)>>>0>50)break E;r=e[124752+(r<<2)>>2];break b}if(Z=1,!(((Qe=(p=l[0|T])>>>3|0)-16|(r>>26)+Qe)>>>0>7))for(;;){if(Z=Z-1|0,(0|(r=p-128|r<<6))>=0){e[f>>2]=0,m&&(e[m>>2]=r),r=1-Z|0;break m}if(!Z)break b;if((192&(p=l[0|(T=T+1|0)]))!=128)break}}e[f>>2]=0,e[56798]=25,r=-1}break m}e[f>>2]=r,r=-2}if((0|r)!=-2){if(T=0,(0|r)==-1)break i;if(A&&(e[(o<<2)+A>>2]=e[s+28>>2],o=o+1|0),!(!E|(0|o)!=(0|c)))break}}if(f=1,r=Ur(A,(c=c<<1|1)<<2))continue;break s}break}if(T=0,c=A,s+296|0&&e[s+296>>2])break i}if(r=e[a+4>>2],(0|(m=e[a+116>>2]))>0|(0|m)>=0&&(r=r-1|0,e[a+4>>2]=r),r=(m=r-e[a+44>>2]|0)+e[a+120>>2]|0,p=e[a+124>>2]+(m>>31)|0,!((p=r>>>0<m>>>0?p+1|0:p)|r)|!(We|(0|r)==(0|ee)&(0|p)==(0|he)))break u;E&&(e[I>>2]=A),(0|ve)!=99&&(c&&(e[(o<<2)+c>>2]=0),T?g[o+T|0]=0:T=0),A=c}o=r=e[a+4>>2]-e[a+44>>2]|0,f=ce+e[a+124>>2]|0,ce=(c=r>>31)+((r=L+e[a+120>>2]|0)>>>0<L>>>0?f+1|0:f)|0,ce=(L=r+o|0)>>>0<r>>>0?ce+1|0:ce,rA=!!(0|I)+rA|0}if(o=t+1|0,t=l[t+1|0])continue;break e}break}A=c;break c}f=1,T=0,A=0;break s}f=E;break A}f=E}if(rA)break A}rA=-1}f&&(xt(T),xt(A))}return J=s+304|0,J=a+144|0,J=i+16|0,rA}function Ct(A){var t,r=0,a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0;J=t=J-16|0;e:{A:{r:{s:{i:{c:{o:{u:{l:{if((A|=0)>>>0<=244){if(3&(r=(c=e[57152])>>>(a=(f=A>>>0<11?16:A+11&-8)>>>3|0)|0)){r=228648+(A=(a=a+(1&~r)|0)<<3)|0,s=e[A+228656>>2],(0|r)!=(0|(A=e[s+8>>2]))?(e[A+12>>2]=r,e[r+8>>2]=A):e[57152]=aa(-2,a)&c,A=s+8|0,r=a<<3,e[s+4>>2]=3|r,e[4+(r=r+s|0)>>2]=1|e[r+4>>2];break e}if((I=e[57154])>>>0>=f>>>0)break l;if(r){r=228648+(A=(s=Ei(0-(A=(0-(A=2<<a)|A)&r<<a)&A))<<3)|0,i=e[A+228656>>2],(0|r)!=(0|(A=e[i+8>>2]))?(e[A+12>>2]=r,e[r+8>>2]=A):(c=aa(-2,s)&c,e[57152]=c),e[i+4>>2]=3|f,s=(A=s<<3)-f|0,e[4+(a=i+f|0)>>2]=1|s,e[A+i>>2]=s,I&&(r=228648+(-8&I)|0,o=e[57157],(A=1<<(I>>>3))&c?A=e[r+8>>2]:(e[57152]=A|c,A=r),e[r+8>>2]=o,e[A+12>>2]=o,e[o+12>>2]=r,e[o+8>>2]=A),A=i+8|0,e[57157]=a,e[57154]=s;break e}if(!(p=e[57153]))break l;for(a=e[228912+(Ei(0-p&p)<<2)>>2],o=(-8&e[a+4>>2])-f|0,r=a;(A=e[r+16>>2])||(A=e[r+20>>2]);)o=(s=(r=(-8&e[A+4>>2])-f|0)>>>0<o>>>0)?r:o,a=s?A:a,r=A;if(m=e[a+24>>2],(0|(s=e[a+12>>2]))!=(0|a)){A=e[a+8>>2],e[A+12>>2]=s,e[s+8>>2]=A;break A}if(!(A=e[(r=a+20|0)>>2])){if(!(A=e[a+16>>2]))break u;r=a+16|0}for(;i=r,s=A,(A=e[(r=A+20|0)>>2])||(r=s+16|0,A=e[s+16>>2]););e[i>>2]=0;break A}if(f=-1,!(A>>>0>4294967231)&&(f=-8&(A=A+11|0),p=e[57153])){o=0-f|0,c=0,f>>>0<256||(c=31,f>>>0>16777215||(c=62+((f>>>38-(A=Ye(A>>>8|0))&1)-(A<<1)|0)|0));n:{p:{if(r=e[228912+(c<<2)>>2])for(A=0,a=f<<((0|c)!=31?25-(c>>>1|0):0);;){if(!((i=(-8&e[r+4>>2])-f|0)>>>0>=o>>>0||(s=r,o=i,i))){o=0,A=r;break p}if(i=e[r+20>>2],r=e[16+((a>>>29&4)+r|0)>>2],A=i?(0|i)==(0|r)?A:i:A,a<<=1,!r)break}else A=0;if(!(A|s)){if(s=0,!(A=(0-(A=2<<c)|A)&p))break l;A=e[228912+(Ei(A&0-A)<<2)>>2]}if(!A)break n}for(;o=(a=(r=(-8&e[A+4>>2])-f|0)>>>0<o>>>0)?r:o,s=a?A:s,A=(r=e[A+16>>2])||e[A+20>>2];);}if(!(!s|e[57154]-f>>>0<=o>>>0)){if(c=e[s+24>>2],(0|s)!=(0|(a=e[s+12>>2]))){A=e[s+8>>2],e[A+12>>2]=a,e[a+8>>2]=A;break r}if(!(A=e[(r=s+20|0)>>2])){if(!(A=e[s+16>>2]))break o;r=s+16|0}for(;i=r,a=A,(A=e[(r=A+20|0)>>2])||(r=a+16|0,A=e[a+16>>2]););e[i>>2]=0;break r}}}if((A=e[57154])>>>0>=f>>>0){s=e[57157],(r=A-f|0)>>>0>=16?(e[4+(a=s+f|0)>>2]=1|r,e[A+s>>2]=r,e[s+4>>2]=3|f):(e[s+4>>2]=3|A,e[4+(A=A+s|0)>>2]=1|e[A+4>>2],a=0,r=0),e[57154]=r,e[57157]=a,A=s+8|0;break e}if((m=e[57155])>>>0>f>>>0){r=m-f|0,e[57155]=r,A=(a=e[57158])+f|0,e[57158]=A,e[A+4>>2]=1|r,e[a+4>>2]=3|f,A=a+8|0;break e}if(A=0,p=f+47|0,e[57270]?a=e[57272]:(e[57273]=-1,e[57274]=-1,e[57271]=4096,e[57272]=4096,e[57270]=t+12&-16^1431655768,e[57275]=0,e[57263]=0,a=4096),(r=(i=p+a|0)&(o=0-a|0))>>>0<=f>>>0||(s=e[57262])&&s>>>0<(c=(a=e[57260])+r|0)>>>0|a>>>0>=c>>>0)break e;l:{if(!(4&l[229052])){n:{p:{C:{m:{if(s=e[57158])for(A=229056;;){if((a=e[A>>2])>>>0<=s>>>0&s>>>0<a+e[A+4>>2]>>>0)break m;if(!(A=e[A+8>>2]))break}if((0|(a=ni(0)))==-1||(c=r,(A=(s=e[57271])-1|0)&a&&(c=(r-a|0)+(A+a&0-s)|0),c>>>0<=f>>>0)||(s=e[57262])&&s>>>0<(o=(A=e[57260])+c|0)>>>0|A>>>0>=o>>>0)break n;if((0|a)!=(0|(A=ni(c))))break C;break l}if((0|(a=ni(c=o&i-m)))==(e[A>>2]+e[A+4>>2]|0))break p;A=a}if((0|A)==-1)break n;if(f+48>>>0<=c>>>0){a=A;break l}if((0|ni(a=(a=e[57272])+(p-c|0)&0-a))==-1)break n;c=a+c|0,a=A;break l}if((0|a)!=-1)break l}e[57263]=4|e[57263]}if((0|(a=ni(r)))==-1|(0|(A=ni(0)))==-1|A>>>0<=a>>>0||(c=A-a|0)>>>0<=f+40>>>0)break s}A=e[57260]+c|0,e[57260]=A,A>>>0>ue[57261]&&(e[57261]=A);l:{if(i=e[57158]){for(A=229056;;){if(((s=e[A>>2])+(r=e[A+4>>2])|0)==(0|a))break l;if(!(A=e[A+8>>2]))break}break c}for((A=e[57156])>>>0<=a>>>0&&A||(e[57156]=a),A=0,e[57265]=c,e[57264]=a,e[57160]=-1,e[57161]=e[57270],e[57267]=0;r=228648+(s=A<<3)|0,e[s+228656>>2]=r,e[s+228660>>2]=r,(0|(A=A+1|0))!=32;);r=(s=c-40|0)-(A=a+8&7?-8-a&7:0)|0,e[57155]=r,A=A+a|0,e[57158]=A,e[A+4>>2]=1|r,e[4+(a+s|0)>>2]=40,e[57159]=e[57274];break i}if(8&l[A+12|0]|s>>>0>i>>>0|a>>>0<=i>>>0)break c;e[A+4>>2]=r+c,a=(A=i+8&7?-8-i&7:0)+i|0,e[57158]=a,A=(r=e[57155]+c|0)-A|0,e[57155]=A,e[a+4>>2]=1|A,e[4+(r+i|0)>>2]=40,e[57159]=e[57274];break i}s=0;break A}a=0;break r}ue[57156]>a>>>0&&(e[57156]=a),r=a+c|0,A=229056;c:{o:{u:{l:{n:{p:{for(;;){if((0|r)!=e[A>>2]){if(A=e[A+8>>2])continue;break p}break}if(!(8&l[A+12|0]))break n}for(A=229056;;){if((r=e[A>>2])>>>0<=i>>>0&&(o=r+e[A+4>>2]|0)>>>0>i>>>0)break l;A=e[A+8>>2]}}if(e[A>>2]=a,e[A+4>>2]=e[A+4>>2]+c,e[4+(p=(a+8&7?-8-a&7:0)+a|0)>>2]=3|f,A=(c=r+(r+8&7?-8-r&7:0)|0)-(m=f+p|0)|0,(0|i)==(0|c)){e[57158]=m,A=e[57155]+A|0,e[57155]=A,e[m+4>>2]=1|A;break o}if(e[57157]==(0|c)){e[57157]=m,A=e[57154]+A|0,e[57154]=A,e[m+4>>2]=1|A,e[A+m>>2]=A;break o}if((3&(o=e[c+4>>2]))==1){i=-8&o;n:if(o>>>0<=255){if(s=e[c+8>>2],r=o>>>3|0,(0|(a=e[c+12>>2]))==(0|s)){e[57152]=e[57152]&aa(-2,r);break n}e[s+12>>2]=a,e[a+8>>2]=s}else{if(f=e[c+24>>2],(0|c)==(0|(a=e[c+12>>2])))if((r=e[(o=c+20|0)>>2])||(r=e[(o=c+16|0)>>2])){for(;s=o,(r=e[(o=(a=r)+20|0)>>2])||(o=a+16|0,r=e[a+16>>2]););e[s>>2]=0}else a=0;else r=e[c+8>>2],e[r+12>>2]=a,e[a+8>>2]=r;if(f){s=e[c+28>>2];p:{if(e[(r=228912+(s<<2)|0)>>2]==(0|c)){if(e[r>>2]=a,a)break p;e[57153]=e[57153]&aa(-2,s);break n}if(e[f+(e[f+16>>2]==(0|c)?16:20)>>2]=a,!a)break n}e[a+24>>2]=f,(r=e[c+16>>2])&&(e[a+16>>2]=r,e[r+24>>2]=a),(r=e[c+20>>2])&&(e[a+20>>2]=r,e[r+24>>2]=a)}}o=e[4+(c=i+c|0)>>2],A=A+i|0}if(e[c+4>>2]=-2&o,e[m+4>>2]=1|A,e[A+m>>2]=A,A>>>0<=255){r=228648+(-8&A)|0,(a=e[57152])&(A=1<<(A>>>3))?A=e[r+8>>2]:(e[57152]=A|a,A=r),e[r+8>>2]=m,e[A+12>>2]=m,e[m+12>>2]=r,e[m+8>>2]=A;break o}if(o=31,A>>>0<=16777215&&(o=62+((A>>>38-(r=Ye(A>>>8|0))&1)-(r<<1)|0)|0),e[m+28>>2]=o,e[m+16>>2]=0,e[m+20>>2]=0,r=228912+(o<<2)|0,(s=e[57153])&(a=1<<o)){for(o=A<<((0|o)!=31?25-(o>>>1|0):0),a=e[r>>2];;){if(r=a,(-8&e[a+4>>2])==(0|A))break u;if(a=o>>>29|0,o<<=1,!(a=e[16+(s=(4&a)+r|0)>>2]))break}e[s+16>>2]=m}else e[57153]=a|s,e[r>>2]=m;e[m+24>>2]=r,e[m+12>>2]=m,e[m+8>>2]=m;break o}for(r=(s=c-40|0)-(A=a+8&7?-8-a&7:0)|0,e[57155]=r,A=A+a|0,e[57158]=A,e[A+4>>2]=1|r,e[4+(a+s|0)>>2]=40,e[57159]=e[57274],e[(s=(A=(o+(o-39&7?39-o&7:0)|0)-47|0)>>>0<i+16>>>0?i:A)+4>>2]=27,A=e[57267],e[s+16>>2]=e[57266],e[s+20>>2]=A,A=e[57265],e[s+8>>2]=e[57264],e[s+12>>2]=A,e[57266]=s+8,e[57265]=c,e[57264]=a,e[57267]=0,A=s+24|0;e[A+4>>2]=7,r=A+8|0,A=A+4|0,r>>>0<o>>>0;);if((0|s)==(0|i))break i;if(e[s+4>>2]=-2&e[s+4>>2],o=s-i|0,e[i+4>>2]=1|o,e[s>>2]=o,o>>>0<=255){r=228648+(-8&o)|0,(a=e[57152])&(A=1<<(o>>>3))?A=e[r+8>>2]:(e[57152]=A|a,A=r),e[r+8>>2]=i,e[A+12>>2]=i,e[i+12>>2]=r,e[i+8>>2]=A;break i}if(A=31,o>>>0<=16777215&&(A=62+((o>>>38-(A=Ye(o>>>8|0))&1)-(A<<1)|0)|0),e[i+28>>2]=A,e[i+16>>2]=0,e[i+20>>2]=0,r=228912+(A<<2)|0,(s=e[57153])&(a=1<<A)){for(A=o<<((0|A)!=31?25-(A>>>1|0):0),s=e[r>>2];;){if((0|o)==(-8&e[(r=s)+4>>2]))break c;if(a=A>>>29|0,A<<=1,!(s=e[16+(a=(4&a)+r|0)>>2]))break}e[a+16>>2]=i}else e[57153]=a|s,e[r>>2]=i;e[i+24>>2]=r,e[i+12>>2]=i,e[i+8>>2]=i;break i}A=e[r+8>>2],e[A+12>>2]=m,e[r+8>>2]=m,e[m+24>>2]=0,e[m+12>>2]=r,e[m+8>>2]=A}A=p+8|0;break e}A=e[r+8>>2],e[A+12>>2]=i,e[r+8>>2]=i,e[i+24>>2]=0,e[i+12>>2]=r,e[i+8>>2]=A}if(!((A=e[57155])>>>0<=f>>>0)){r=A-f|0,e[57155]=r,A=(a=e[57158])+f|0,e[57158]=A,e[A+4>>2]=1|r,e[a+4>>2]=3|f,A=a+8|0;break e}}e[56798]=48,A=0;break e}r:if(c){r=e[s+28>>2];s:{if(e[(A=228912+(r<<2)|0)>>2]==(0|s)){if(e[A>>2]=a,a)break s;p=aa(-2,r)&p,e[57153]=p;break r}if(e[c+(e[c+16>>2]==(0|s)?16:20)>>2]=a,!a)break r}e[a+24>>2]=c,(A=e[s+16>>2])&&(e[a+16>>2]=A,e[A+24>>2]=a),(A=e[s+20>>2])&&(e[a+20>>2]=A,e[A+24>>2]=a)}r:if(o>>>0<=15)A=o+f|0,e[s+4>>2]=3|A,e[4+(A=A+s|0)>>2]=1|e[A+4>>2];else if(e[s+4>>2]=3|f,e[4+(i=s+f|0)>>2]=1|o,e[i+o>>2]=o,o>>>0<=255)r=228648+(-8&o)|0,(a=e[57152])&(A=1<<(o>>>3))?A=e[r+8>>2]:(e[57152]=A|a,A=r),e[r+8>>2]=i,e[A+12>>2]=i,e[i+12>>2]=r,e[i+8>>2]=A;else{A=31,o>>>0<=16777215&&(A=62+((o>>>38-(A=Ye(o>>>8|0))&1)-(A<<1)|0)|0),e[i+28>>2]=A,e[i+16>>2]=0,e[i+20>>2]=0,r=228912+(A<<2)|0;s:{if((a=1<<A)&p){for(A=o<<((0|A)!=31?25-(A>>>1|0):0),f=e[r>>2];;){if((-8&e[(r=f)+4>>2])==(0|o))break s;if(a=A>>>29|0,A<<=1,!(f=e[16+(a=(4&a)+r|0)>>2]))break}e[a+16>>2]=i}else e[57153]=a|p,e[r>>2]=i;e[i+24>>2]=r,e[i+12>>2]=i,e[i+8>>2]=i;break r}A=e[r+8>>2],e[A+12>>2]=i,e[r+8>>2]=i,e[i+24>>2]=0,e[i+12>>2]=r,e[i+8>>2]=A}A=s+8|0;break e}A:if(m){r=e[a+28>>2];r:{if(e[(A=228912+(r<<2)|0)>>2]==(0|a)){if(e[A>>2]=s,s)break r;e[57153]=aa(-2,r)&p;break A}if(e[m+(e[m+16>>2]==(0|a)?16:20)>>2]=s,!s)break A}e[s+24>>2]=m,(A=e[a+16>>2])&&(e[s+16>>2]=A,e[A+24>>2]=s),(A=e[a+20>>2])&&(e[s+20>>2]=A,e[A+24>>2]=s)}o>>>0<=15?(A=o+f|0,e[a+4>>2]=3|A,e[4+(A=A+a|0)>>2]=1|e[A+4>>2]):(e[a+4>>2]=3|f,e[4+(s=a+f|0)>>2]=1|o,e[s+o>>2]=o,I&&(r=228648+(-8&I)|0,i=e[57157],(A=1<<(I>>>3))&c?A=e[r+8>>2]:(e[57152]=A|c,A=r),e[r+8>>2]=i,e[A+12>>2]=i,e[i+12>>2]=r,e[i+8>>2]=A),e[57157]=s,e[57154]=o),A=a+8|0}return J=t+16|0,0|A}function $t(A,t,r,a,s){var i,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0;J=i=J-544|0,jA(i+320|0,0,100),jA(i+208|0,0,100),m=r?e[r>>2]:m,L=e[A+12>>2],p=e[36115];e:{A:{r:{s:{for(;;){if(o=(0|(o=l[t+c|0]))>=(0|p)?13:o,g[i+c|0]=o,!o){o=c;break s}if(f=(0|p)<=(0|(f=l[(o=1|c)+t|0]))?13:f,g[o+i|0]=f,!f)break s;if((0|(c=c+2|0))==200)break}c=198,ee=l[i+199|0];break r}if(!o)break A;ee=l[i+(c=o-1|0)|0],(0|o)!=1&&(c=o-2|0)}if(Qe=8&m,e[i+536>>2]=(Qe>>>3|0?3:7)&m,m=l[i+c|0],p=1,E=(0|(I=mi(A,i,i+432|0,i+540|0,i+536|0,1)))>=0?I:0,he=l[0|i])for(c=i,o=he;o=e[144464+((255&o)<<2)>>2],l[o+11|0]==2&&(1048576&(o=e[o+4>>2])||(f=(2097152&o)>>>21|0,f|=o=l[e[144464+(l[0|(T=c+1|0)]<<2)>>2]+10|0]==12,g[(i+208|0)+p|0]=f,Z=e[144464+(l[(o?2:1)+c|0]<<2)>>2],f=l[Z+11|0]-10>>>0<4294967289|!(32&l[Z+6|0])&l[e[144464+(l[(o?3:2)+c|0]<<2)>>2]+11|0]==2?f:f?2:1,c=o?T:c,g[(i+320|0)+p|0]=f,p=p+1|0)),o=l[0|(c=c+1|0)];);c=r=r?E:I;r:{s:{i:{c:{o:{u:{l:{n:{p:{C:{m:{b:switch(e[A+8>>2]-1|0){case 11:if((0|(r=e[i+540>>2]))<2)break p;if(c=1,E=1&(o=r-1|0),(0|r)!=2)break m;p=0;break C;case 8:if((0|(o=e[i+540>>2]))<2)break s;if(p=3&(f=o-1|0),c=1,o-2>>>0>=3)for(E=-4&f,f=0;m=g[0|(o=(i+432|0)+c|0)],g[0|o]=(0|m)<0?4:m,m=g[o+1|0],g[o+1|0]=(0|m)<0?4:m,m=g[o+2|0],g[o+2|0]=(0|m)<0?4:m,m=o,o=g[o+3|0],g[m+3|0]=(0|o)<0?4:o,c=c+4|0,(0|E)!=(0|(f=f+4|0)););if(!p)break s;for(o=0;f=g[0|(m=(i+432|0)+c|0)],g[0|m]=(0|f)<0?4:f,c=c+1|0,(0|p)!=(0|(o=o+1|0)););break s;case 7:if(!l[i+322|0]|g[i+321|0]>0)break s;case 0:if(e[i+536>>2]|e[i+540>>2]<3)break s;if(e[i+536>>2]=2,c=4,r)break r;g[i+434|0]=4;break r;case 1:if(e[i+536>>2])break r;h:{E:{if((0|(p=e[i+540>>2]))>=3){r=p-2|0,e[i+536>>2]=r,c=r;w:if(512&L&&(o=e[144464+(ee<<2)>>2],(0|(f=l[o+11|0]))!=2)){c=e[o>>2],o=i;y:{L:{if((0|(E=e[A+212>>2]))!=26977){if((0|E)!=24942)break L;switch(c-110|0){case 0:case 5:if(c=r,l[e[144464+(m<<2)>>2]+11|0]==2)break w}c=p-1|0;break y}if((0|c)==115&&(c=r,l[e[144464+(m<<2)>>2]+11|0]==2))break w;c=p-1|0;break y}L:{W:{if((0|c)==115){if(c=r,(0|(f=l[e[144464+(m<<2)>>2]+11|0]))!=8)break W;break w}if((0|f)!=8)break L;f=l[e[144464+(m<<2)>>2]+11|0]}if(c=r,(255&f)==2)break w}c=p-1|0}e[o+536>>2]=c}if(524288&L&&(g[(o=p-1|0)+(p=i+208|0)|0]<=g[r+p|0]||(e[i+536>>2]=o,c=o)),l[(i+432|0)+c|0]>1){o=c;break h}if(o=2,r=c-1|0,c>>>0>=2)break E;e[i+536>>2]=c+1;break h}r=1}o=r,e[i+536>>2]=o}if(c=4,g[0|(r=(i+432|0)+o|0)]>=0||g[(o=(i+432|0)+o|0)-1|0]>=4&g[o+1|0]>3)break r;g[0|r]=4;break r;case 2:if(e[i+536>>2])break r;for(r=(c=e[i+540>>2])-1|0,r&=r>>31;;){if((0|(c=c-1|0))<=0)break i;if(!(g[0|(o=(i+432|0)+c|0)]>=0))break}e[i+536>>2]=c,c=4,g[0|o]=4;break r;case 3:if(e[i+536>>2])break s;if(o=(0|(c=e[i+540>>2]-3|0))<=1?1:c,e[i+536>>2]=o,c=4,r)break r;g[o+(i+432|0)|0]=4;break r;case 4:if(e[i+536>>2])break r;if(o=(r=e[i+540>>2])-3|0,e[i+536>>2]=o,(0|r)<=15){h:{switch(l[e[144464+(ee<<2)>>2]+11|0]-2|0){case 0:o=g[r+94176|0];break h;case 2:o=g[r+94192|0];break h}o=g[r+94160|0]}e[i+536>>2]=o}c=4,g[(i+432|0)+o|0]=4;break r;case 5:if(e[i+536>>2])break r;if(p=-1,f=0,!((0|(r=(o=e[i+540>>2])-1|0))<2)){if(c=1,T=1&o,(0|o)!=3)for(Z=(-2&o)-4|0,o=0;g[(i+432|0)+c|0]<0&&(f=(m=(0|(E=g[(i+320|0)+c|0]))<(0|p))?f:c,p=m?p:E),g[(m=c+1|0)+(i+432|0)|0]<0&&(f=(E=(0|(ve=g[m+(i+320|0)|0]))<(0|p))?f:m,p=E?p:ve),c=c+2|0,m=(0|o)!=(0|Z),o=o+2|0,m;);!T|g[(i+432|0)+c|0]>=0||(p=(o=(0|(m=g[(i+320|0)+c|0]))<(0|p))?p:m,f=o?f:c)}e[i+536>>2]=f,l[r+(i+320|0)|0]!=2|(0|p)>1?(0|p)>0||(f=1,e[i+536>>2]=1):(e[i+536>>2]=r,f=r),c=4,g[(i+432|0)+f|0]=4;break r;case 14:break c;case 12:break o;case 6:break b;default:break r}if(e[i+536>>2])break r;p=(r=e[i+540>>2])-1|0,e[i+536>>2]=p;b:if(!((0|r)<2))for(c=1;;){if(l[(i+432|0)+c|0]==1){p=c-1|0,e[i+536>>2]=p;break b}if((0|r)==(0|(c=c+1|0)))break}c=4,g[(i+432|0)+p|0]=4;break r}for(T=-2&o,p=0,f=0;o=l[0|(Z=(m=i+432|0)+c|0)],ce=Z,ve=g[(Z=i+208|0)+c|0]>0,g[0|ce]=ve||(0|o)==4?3:o,We=(0|(m=l[0|(ce=(o=c+1|0)+m|0)]))==4?3:m,m=g[o+Z|0]>0,g[0|ce]=m?3:We,p=m?o:ve?c:p,c=c+2|0,(0|T)!=(0|(f=f+2|0)););}if(E&&(m=(0|(o=l[0|(f=(i+432|0)+c|0)]))==4?3:o,o=g[(i+208|0)+c|0]>0,g[0|f]=o?3:m,p=o?c:p),o=e[i+536>>2])break u;if((0|p)>0){e[i+536>>2]=p,o=p;break u}if((0|r)<6)break n;o=r-3|0;break l}if(o=e[i+536>>2])break u}o=r-1|0}e[i+536>>2]=o}c=4,g[(i+432|0)+o|0]=4;break r}if(e[i+536>>2])break r;o=1,e[i+536>>2]=1,l[i+209|0]|e[i+540>>2]<3|g[i+210|0]<=0||(o=2,e[i+536>>2]=2),c=4,g[i+432|o]=4;break r}if(e[i+536>>2]||(0|(o=e[i+540>>2]))<3)break s;if(jA(i+432|1,0,o-1|0),e[i+536>>2]=2,r||(g[i+434|0]=4),c=4,o>>>0<4)break r;g[431+(o+i|0)|0]=3;break r}e[i+536>>2]=r,c=4;break r}c=r}!(256&L)|2&s||(0|(r=e[i+540>>2]))<3|(0|I)>2||l[0|(o=(r=r+(i+432|0)|0)-1|0)]!=4|l[e[144464+(ee<<2)>>2]+11|0]!=2||(g[0|o]=1,g[r-2|0]=4);r:{s:{if(Qe)I=e[i+540>>2];else{if(r=g[i+433|0],!(!(4096&L)|(0|(I=e[i+540>>2]))!=3)){if((0|r)==4){g[i+434|0]=3;break s}if(l[i+434|0]==4){g[i+433|0]=3;break s}}if(!(!(8192&L)|(0|r)>=0|(0|I)<4|g[i+434|0]<4)){g[i+433|0]=3;break s}}if(f=0,(0|I)<2)break r}for(p=(0|c)<4?4:3,Z=128&L,ve=64&L,ce=32&L,E=I-1|0,We=16&L,Ke=!(32768&L),ee=0,m=0,c=1;;){s:{i:if((0|(o=g[0|(T=(i+432|0)+c|0)]))>=0)f=p;else{f=3;c:{o:if(!(!(!We|(0|p)>3)&(0|c)==(0|E))){if(!(1&(ee|Ke)))break c;if(!(g[431+(i+c|0)|0]>1)){if((0|(o=g[(r=c+1|0)+(i+432|0)|0]))>=2){if((0|p)!=4||(f=4,o>>>0>=3))break o}else if(!(!ce|(0|p)!=3)){p=3;break s}if(!ve|c>>>0<2||(f=l[(i+320|0)+c|0]))break c;if((0|E)>(0|(o=c))){for(;;){if(g[(i+320|0)+o|0]>0)break s;if((0|E)==(0|(o=o+1|0)))break}if(f)break c}if(g[r+(i+320|0)|0]<=0)break c;break s}f=p}o=l[0|T];break i}g[0|T]=p,ee=1,f=3,o=p}i:{if(o<<24>>24>=4){if(r=m||c,!m|!Z)break i;g[0|T]=3}p=f;break s}p=f,m=r}if(f=1,(0|I)==(0|(c=c+1|0)))break}}if(a=!Qe|(0|a)>=0?a:e[((0|I)<3?16:20)+A>>2],p=0,o=0,f){if(ee=3&(r=I-1|0),f=0,I-2>>>0<3)c=1;else for(Qe=-4&r,c=1,m=0;o=(r=(0|(o=(T=(0|(o=(E=(0|(o=(I=(0|(E=g[(r=i+432|0)+c|0]))<(0|o))?o:E))>(0|(T=g[(Z=c+1|0)+r|0])))?o:T))>(0|(ce=g[(ve=c+2|0)+r|0])))?o:ce))>(0|(We=g[(ce=c+3|0)+r|0])))?o:We,p=r?T?E?I?p:c:Z:ve:ce,c=c+4|0,(0|Qe)!=(0|(m=m+4|0)););if(ee)for(;o=(r=(0|(m=g[(i+432|0)+c|0]))<(0|o))?o:m,p=r?p:c,c=c+1|0,(0|ee)!=(0|(f=f+1|0)););}if((0|a)<0?a=o:(0|a)<=(0|o)&(0|o)>4||(g[(i+432|0)+p|0]=a),ee=t+197|0,I=1,!(1&s)&&(r=e[144464+(he<<2)>>2])){if(o=i,!((0|(f=l[r+11|0]))!=1&(0|he)!=15))for(;r=l[0|(o=o+1|0)],(0|(f=l[e[144464+(r<<2)>>2]+11|0]))==1|(0|r)==15;);!(48&(r=e[A+4>>2]))|(0|f)!=2||(g[0|t]=(32&r)>>>5|0&&g[i+433|0]>3?11:23,t=t+1|0)}r:if(!(t>>>0>=ee>>>0))for(E=65536&L,T=2&L,Qe=4&L,c=i;;){if(!(o=l[0|c]))break r;if(s=c,c=c+1|0,r=e[144464+(o<<2)>>2]){s:{i:{switch(l[r+11|0]){case 0:e[A+8200>>2]=0;break s;case 2:if(!(16&l[r+6|0]))break i}if(l[0|c]!=20)break s}if((0|(he=e[i+540>>2]))<(0|I))break e;m=g[0|(L=(i+432|0)+I|0)],e[A+8200>>2]=m;i:{c:{if(!((0|(r=m))>1)){if(f=he-1|0,!(!Qe|(0|I)<2|(0|a)<2)&&(r=0,(0|f)==(0|I)))break c;if(r=1,!((0|I)==1|T|(he-2|0)==(0|I)&g[f+(i+432|0)|0]<2|(0|f)==(0|I)||g[431+(i+I|0)|0]>=0&&(r=m,E))){r=0,g[0|L]=0;break c}}if(r&&(0|r)<2)break i}g[0|t]=l[r+94151|0],t=t+1|0,m=g[0|L]}f=(0|a)>(0|m),l[0|c]==12&&1&(he=e[A+28>>2])&&(c=(16&he?(0|p)!=(0|I):(0|r)<4)?s+2|0:c),a=f?a:m,I=I+1|0}if((0|o)!=1&&(g[0|t]=o,t=t+1|0),!(t>>>0<ee>>>0))break}}g[0|t]=0}return void(J=i+544|0)}Pe(86136,86634,1353,94208),xe()}function Qr(A,t,r,a,s,i,o,c,f){var m,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0,MA=0,zA=0,et=0,at=0,dt=0,Ft=0,Zt=0,er=0,pr=0,hr=0,yr=0,Er=0,Fr=0,Wr=0,ia=0;J=m=J-336|0,T=c,Z=65535&f,L=a,E=65535&s,Qe=-2147483648&(s^f);e:{if(!((he=f>>>16&32767)-32767>>>0>4294934529&(ee=s>>>16&32767)-32767>>>0>=4294934530)){if(!(!a&(0|(p=2147483647&s))==2147418112?!(t|r):p>>>0<2147418112)){ce=a,Qe=32768|s;break e}if(!(!c&(0|(s=2147483647&f))==2147418112?!(i|o):s>>>0<2147418112)){ce=c,Qe=32768|f,t=i,r=o;break e}if(!(t|a|2147418112^p|r)){if(!(i|c|2147418112^s|o)){t=0,r=0,Qe=2147450880;break e}Qe|=2147418112,t=0,r=0;break e}if(!(i|c|2147418112^s|o)){t=0,r=0;break e}if(!(t|a|r|p)){ce=(t=!(i|c|s|o))?0:ce,Qe=t?2147450880:Qe,t=0,r=0;break e}if(!(i|c|s|o)){Qe|=2147418112,t=0,r=0;break e}(0|p)==65535|p>>>0<65535&&(f=a=!(E|L),p=a?t:L,c=a<<=6,ea(m+320|0,t,r,L,E,(a=a+((0|(f=Ye(f?r:E)))==32?Ye(p)+32|0:f)|0)-15|0),We=16-a|0,L=e[m+328>>2],E=e[m+332>>2],r=e[m+324>>2],t=e[m+320>>2]),s>>>0>65535||(c=a=!(T|Z),f=a?i:T,s=a<<=6,ea(m+304|0,i,o,T,Z,(a=a+((0|(c=Ye(c?o:Z)))==32?Ye(f)+32|0:c)|0)-15|0),We=(a+We|0)-16|0,T=e[m+312>>2],Z=e[m+316>>2],i=e[m+304>>2],o=e[m+308>>2])}if(zA=s=65536|Z,et=T,p=s<<15|(a=T)>>>17,Kr(m+288|0,a=s=a<<15|o>>>17,c=p,0,0,f=0-a|0,p=1963258675-(p+!!(0|a)|0)|0,0,0),Kr(m+272|0,0-(a=e[m+296>>2])|0,0-(e[m+300>>2]+!!(0|a)|0)|0,0,0,f,p,0,0),Kr(m+256|0,f=(a=e[m+280>>2])<<1|e[m+276>>2]>>>31,a=e[m+284>>2]<<1|a>>>31,0,0,s,c,0,0),Kr(m+240|0,f,a,0,0,0-(p=e[m+264>>2])|0,0-(e[m+268>>2]+!!(0|p)|0)|0,0,0),Kr(m+224|0,p=(f=e[m+248>>2])<<1|e[m+244>>2]>>>31,a=e[m+252>>2]<<1|f>>>31,0,0,s,c,0,0),Kr(m+208|0,p,a,0,0,0-(f=e[m+232>>2])|0,0-(e[m+236>>2]+!!(0|f)|0)|0,0,0),Kr(m+192|0,f=(a=e[m+216>>2])<<1|e[m+212>>2]>>>31,a=e[m+220>>2]<<1|a>>>31,0,0,s,c,0,0),Kr(m+176|0,f,a,0,0,0-(p=e[m+200>>2])|0,0-(e[m+204>>2]+!!(0|p)|0)|0,0,0),Kr(m+160|0,f=s,a=c,0,0,c=(T=(s=e[m+184>>2])<<1|e[m+180>>2]>>>31)-1|0,s=(e[m+188>>2]<<1|s>>>31)-!T|0,0,0),Kr(m+144|0,i<<15,o<<15|i>>>17,0,0,a=c,s,0,0),ve=m+112|0,cA=e[m+168>>2],c=e[m+172>>2],I=(T=e[m+160>>2])+(f=e[m+152>>2])|0,p=(Z=e[m+164>>2])+e[m+156>>2]|0,f=p=f>>>0>I>>>0?p+1|0:p,p=(p=(0|Z)==(0|p)&I>>>0<T>>>0|p>>>0<Z>>>0)>>>0>(Z=p+cA|0)>>>0?c+1|0:c,Kr(ve,a,s,0,0,0-(c=(T=!f&I>>>0>1|!!(0|f))+Z|0)|0,0-(!!(0|c)+(p=T>>>0>c>>>0?p+1|0:p)|0)|0,0,0),Kr(m+128|0,1-I|0,0-((I>>>0>1)+f|0)|0,0,0,a,s,0,0),pr=(ee-he|0)+We|0,ve=s=e[m+116>>2],T=(a=e[m+112>>2])<<1,ee=p=s<<1|a>>>31,a=p,MA=c=e[m+140>>2],a=a+(p=c<<1|(s=e[m+136>>2])>>>31)|0,s=a=(c=(f=s<<1|e[m+132>>2]>>>31)+T|0)>>>0<f>>>0?a+1|0:a,rA=a=a-(c>>>0<13927)|0,cA=a,hr=p=65536|E,yr=L,Fr=(a=L)<<1,Wr=p=p<<1|a>>>31,dt=p,Ft=a=It(rA,I=0,p,0),Ke=p=$,We=t<<1,he=a=r<<1|t>>>31,Z=p=0,rA=(0|s)==(0|rA)&(f=c-13927|0)>>>0<c>>>0|s>>>0>rA>>>0,s=(0|s)==(0|ee)&c>>>0<T>>>0|s>>>0<ee>>>0,a=e[m+120>>2],p=c=e[m+124>>2]<<1|a>>>31,p=(a=(I=MA>>>31|0)+(a=a<<1|ve>>>31)|0)>>>0<I>>>0?p+1|0:p,I=(c=a)>>>0>(a=a+s|0)>>>0?p+1|0:p,I=(s=a)>>>0>(a=a+rA|0)>>>0?I+1|0:I,s=a-1|0,p=It(he,Z,rA=I-!a|0,ee=0),c=$+Ke|0,ve=(0|Ke)==(0|(c=(a=p+Ft|0)>>>0<p>>>0?c+1|0:c))&a>>>0<Ft>>>0|c>>>0<Ke>>>0,MA=s,s=It(s,p=0,at=(Er=r>>>31|0)|L<<1,Ke=0),p=$+c|0,I=0,T=p=s>>>0>(L=s+a|0)>>>0?p+1|0:p,I=(s=a=(0|p)==(0|c)&a>>>0>L>>>0|c>>>0>p>>>0)>>>0>(a=a+ve|0)>>>0?1:I,s=It(dt,Z,rA,ee),p=$+I|0,ve=a=s+a|0,a=a>>>0<s>>>0?p+1|0:p,s=It(dt,Z,MA,Ke),E=$,c=s,s=It(at,Ke,rA,ee),p=$+E|0,s=p=s>>>0>(I=c+s|0)>>>0?p+1|0:p,a=a+(p=(0|E)==(0|p)&c>>>0>I>>>0|p>>>0<E>>>0)|0,ve=E=ve+s|0,E=a=E>>>0<s>>>0?a+1|0:a,p=I+T|0,s=p=(a=(s=0)+L|0)>>>0<s>>>0?p+1|0:p,c=(0|p)==(0|T)&a>>>0<L>>>0|p>>>0<T>>>0,p=E,I=c,Zt=c=c+ve|0,I=p=I>>>0>c>>>0?p+1|0:p,ve=a,E=a,L=s,Ft=f,a=It(f,0,at,Ke),c=$,s=a,f=It(cA,ce,he,ce),p=$+c|0,f=(0|c)==(0|(p=(a=a+f|0)>>>0<f>>>0?p+1|0:p))&a>>>0<s>>>0|c>>>0>p>>>0,c=p,s=It(MA,Ke,er=-2&We,0),p=$+p|0,s=p=s>>>0>(T=s+a|0)>>>0?p+1|0:p,a=(0|p)==(0|c)&a>>>0>T>>>0|c>>>0>p>>>0,c=0,a=((f=a+f|0)>>>0<a>>>0?1:c)+L|0,p=I,f=a=(E=f+E|0)>>>0<f>>>0?a+1|0:a,c=a=(0|a)==(0|L)&E>>>0<ve>>>0|a>>>0<L>>>0,ia=a=a+Zt|0,ve=p=c>>>0>a>>>0?p+1|0:p,a=It(dt,Z,Ft,ce),Zt=$,dt=a,c=It(rA,ee,er,ce),p=$+Zt|0,L=a=a+c|0,I=a+(Z=It(cA,ce,at,Ke))|0,a=(c=a>>>0<c>>>0?p+1|0:p)+$|0,a=I>>>0<Z>>>0?a+1|0:a,ee=I,Z=I+(p=It(he,ce,MA,Ke))|0,I=$+a|0,at=(0|a)==(0|(I=p>>>0>Z>>>0?I+1|0:I))&Z>>>0<ee>>>0|a>>>0>I>>>0,p=((a=(a=(a=(0|a)==(0|c)&L>>>0>ee>>>0|a>>>0<c>>>0)+(p=(0|c)==(0|Zt)&L>>>0<dt>>>0|c>>>0<Zt>>>0)|0)+at|0)|(MA=0))+f|0,L=p=(c=I)>>>0>(ee=c+E|0)>>>0?p+1|0:p,a=(0|f)==(0|p)&E>>>0>ee>>>0|f>>>0>p>>>0,p=ve,c=a,MA=a=a+ia|0,f=p=c>>>0>a>>>0?p+1|0:p,a=It(cA,ce,er,ce),cA=$,E=a,c=It(he,ce,Ft,ce),p=$+cA|0,c=(0|(p=(a=a+c|0)>>>0<c>>>0?p+1|0:p))==(0|cA)&a>>>0<E>>>0|p>>>0<cA>>>0,E=p,a=p+T|0,p=(c|(ve=0))+s|0,E=(0|s)==(0|(p=a>>>0<E>>>0?p+1|0:p))&a>>>0<T>>>0|s>>>0>p>>>0,I=(c=p)+(p=Z)|0,p=0,p=((s=a=(0|c)==(0|(I=(T=(Z=0)+a|0)>>>0<Z>>>0?I+1|0:I))&a>>>0>T>>>0|c>>>0>I>>>0)>>>0>(a=a+E|0)>>>0?1:p)+L|0,I=f,s=p=(s=a)>>>0>(a=a+ee|0)>>>0?p+1|0:p,f=I=(f=c=(0|L)==(0|p)&a>>>0<ee>>>0|p>>>0<L>>>0)>>>0>(c=c+MA|0)>>>0?I+1|0:I,(0|I)==131071|I>>>0<131071?(yr=Fr|Er,hr=Ke|Wr,Kr(m+80|0,a,s,c,f,i,o,et,zA),Z=I=e[m+84>>2],p=t<<17,L=(r=(T=0)-(E=e[m+88>>2])|0)-(I=!!(I|(t=e[m+80>>2])))|0,E=(p-(e[m+92>>2]+(E>>>0>T>>>0)|0)|0)-(r>>>0<I>>>0)|0,T=0-t|0,Z=0-(!!(0|t)+Z|0)|0,t=pr+16382|0):(Kr(m+96|0,a=(1&s)<<31|a>>>1,s=c<<31|s>>>1,c=(1&f)<<31|c>>>1,f=f>>>1|0,i,o,et,zA),he=L=e[m+100>>2],L=(T=0-(We=e[m+104>>2])|0)-(E=!!(L|(I=e[m+96>>2])))|0,E=((t<<16)-(e[m+108>>2]+(Z>>>0<We>>>0)|0)|0)-(E>>>0>T>>>0)|0,T=0-I|0,Z=0-(!!(0|I)+he|0)|0,We=t,he=r,t=pr+16383|0),(0|t)>=32767)Qe|=2147418112,t=0,r=0;else{if((0|t)>0)I=E<<1|L>>>31,L=L<<1|Z>>>31,E=I,We=c,he=65535&f|t<<16,I=Z<<1|T>>>31,f=T<<1;else{if((0|t)<=-113){t=0,r=0;break e}Ya(m- -64|0,a,s,c,f,1-t|0),ea(m+48|0,We,he,yr,hr,t+112|0),Kr(m+32|0,i,o,et,zA,a=e[m+64>>2],s=e[m+68>>2],We=e[m+72>>2],he=e[m+76>>2]),t=e[m+40>>2],T=(r=e[m+56>>2])-(L=t<<1|(I=e[m+36>>2])>>>31)|0,E=e[m+60>>2]-((e[m+44>>2]<<1|t>>>31)+(r>>>0<L>>>0)|0)|0,p=(t=e[m+32>>2])<<1,L=T-(t=(0|(c=I<<1|t>>>31))==(0|(f=e[m+52>>2]))&p>>>0>(r=e[m+48>>2])>>>0|c>>>0>f>>>0)|0,E=E-(t>>>0>T>>>0)|0,I=f-((r>>>0<p>>>0)+c|0)|0,f=r-p|0}t=f,Kr(m+16|0,i,o,et,zA,3,0,0,0),Kr(m,i,o,et,zA,5,0,0,0),c=I+(r=0)|0,c=f>>>0>(t=t+(p=1&a)|0)>>>0?c+1|0:c,f=t,o=(0|o)==(0|c)&t>>>0>i>>>0|o>>>0<c>>>0,I=E,t=(0|r)==(0|c)&t>>>0<p>>>0|r>>>0>c>>>0,p=s,r=p=(r=t=(t=(0|(I=t>>>0>(i=t+L|0)>>>0?I+1|0:I))==(0|zA))&(0|i)==(0|et)?o:t&i>>>0>et>>>0|I>>>0>zA>>>0)>>>0>(t=t+a|0)>>>0?p+1|0:p,a=(0|s)==(0|p)&t>>>0<a>>>0|s>>>0>p>>>0,p=he,p=(s=a)>>>0>(a=a+We|0)>>>0?p+1|0:p,o=a,E=(0|(s=e[m+20>>2]))==(0|c)&ue[m+16>>2]<f>>>0|s>>>0<c>>>0,s=e[m+28>>2],s=p>>>0<2147418112&((0|(a=e[m+24>>2]))==(0|i)&(0|s)==(0|I)?E:(0|s)==(0|I)&a>>>0<i>>>0|s>>>0<I>>>0),a=r,p=(r=t=(0|r)==(0|(a=(E=s)>>>0>(s=t+s|0)>>>0?a+1|0:a))&t>>>0>s>>>0|r>>>0>a>>>0)>>>0>(t=t+o|0)>>>0?p+1|0:p,o=t,c=(0|(r=e[m+4>>2]))==(0|c)&ue[m>>2]<f>>>0|r>>>0<c>>>0,r=e[m+12>>2],r=I=(r=t=p>>>0<2147418112&((0|(t=e[m+8>>2]))==(0|i)&(0|r)==(0|I)?c:(0|r)==(0|I)&t>>>0<i>>>0|r>>>0<I>>>0))>>>0>(t=t+s|0)>>>0?a+1|0:a,s=(0|a)==(0|I)&t>>>0<s>>>0|a>>>0>I>>>0,a=p,i=s,ce|=s=s+o|0,Qe|=a=i>>>0>s>>>0?a+1|0:a}}e[A>>2]=t,e[A+4>>2]=r,e[A+8>>2]=ce,e[A+12>>2]=Qe,J=m+336|0}function tr(A,t,r,a,s){var i,o,c,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0,MA=0,zA=0,et=0,at=0;if(J=o=J-80|0,c=e[r+8>>2],!s|!M[r+4>>1]||(e[s+8>>2]=0),i=jA(a,0,152),e[i+44>>2]=l[c+14|0],e[i+40>>2]=l[c+15|0],a=M[c+8>>1]){for(a=e[34459]+(a<<1)|0,ce=256&t,he=r+32|0,We=r-32|0,Ke=r- -64|0,rA=r+96|0,cA=r+-64|0,MA=r-28|0,zA=1&t,et=r-24|0,ve=e[30450];;){f=15&(E=(t=M[a>>1])>>>8|0);e:{A:{r:{s:{i:{c:{o:{u:{l:{n:{p:{C:switch(0|(p=t>>>12|0)){case 10:break o;case 9:break u;case 6:break l;case 2:case 3:break n;case 1:break p;case 0:break C;case 11:case 12:case 13:case 14:case 15:break c;default:break i}m=255&t;C:{m:{b:switch(0|f){case 13:if(m)break m;f=a,t=0;break C;case 0:switch(f=a,m-1|0){case 1:break e;case 0:break A}e[o+20>>2]=v0(o+75|0,e[c>>2]),e[o+16>>2]=t,fr(ve,85851,o+16|0);break e;case 5:if(l[e[144464+(l[r+34|0]<<2)>>2]+11|0]!=2)break e;e[i+20>>2]=m;break e;case 12:break b;default:break s}e[i+44>>2]=e[i+44>>2]+(t<<24>>31&-256|m);break e}g[i+132|0]=l[a+3|0],f=a+2|0,g[i+133|0]=l[0|f],t=2,m>>>0<3||(g[i+134|0]=l[a+5|0],f=a+4|0,g[i+135|0]=l[0|f],t=4,m>>>0<5||(g[i+136|0]=l[a+7|0],f=a+6|0,g[i+137|0]=l[0|f],t=6,m>>>0<7||(g[i+138|0]=l[a+9|0],f=a+8|0,g[i+139|0]=l[0|f],t=8,m>>>0<9||(g[i+140|0]=l[a+11|0],f=a+10|0,g[i+141|0]=l[0|f],t=10,m>>>0<11||(g[i+142|0]=l[a+13|0],f=a+12|0,g[i+143|0]=l[0|f],t=12,m>>>0<13||(g[i+144|0]=l[a+15|0],f=a+14|0,g[i+145|0]=l[0|f],t=14,m>>>0<15||(g[i+146|0]=l[a+17|0],f=a+16|0,g[i+147|0]=l[0|f],t=16)))))))}g[132+(t+i|0)|0]=0,m=L;break A}if(!A|f>>>0>7||(p=r,l[e[144464+(l[r+2|0]<<2)>>2]+11|0]!=2&&(p=he,l[e[144464+(l[r+34|0]<<2)>>2]+11|0]!=2))||!(1&(f=e[A+56>>2]))&&16&l[0|r])break e;m=15&l[p+3|0],m=2&f&&l[p+6|0]<=m>>>0?4:m;p:{C:{m:{b:switch((f=7&E)-3|0){case 1:break C;case 0:break b;default:break m}if(m>>>0>3)break p;break e}if(e[102832+(f<<2)>>2]>(0|m))break p;break e}if(l[p+6|0]>m>>>0)break e}e[i+8>>2]=255&t,m=1;break r}if((57344&t)==8192){for(at=e[32972],E=1,Qe=0;;){I=255&t,f=(T=4095&t)>>>8|0;n:if(T>>>0<=3583){(0|(m=(f>>>0)%7|0))==6&&(m=M[a+2>>1]),ee=0,f=r;p:{C:{m:switch(0|m){case 6:if(p=0,M[r+36>>1]|M[r+68>>1])break n;case 3:f=Ke;break C;case 9:if(p=0,M[r+36>>1]|M[r+68>>1])break n;if(f=rA,!M[r+100>>1])break p;break n;case 7:if(p=0,M[r+36>>1])break n;for(m=1;;){if(l[e[144464+(l[2+(f=(m<<5)+r|0)|0]<<2)>>2]+11|0]==2)break C;if(M[4+(((m=m+1|0)<<5)+r|0)>>1])break}break n;case 5:if(p=0,M[r+4>>1])break n;case 0:ee=1,f=We;break C;case 4:if(p=0,M[r+36>>1])break n;case 2:f=he;break C;case 8:if(p=0,!s)break n;if(ee=1,e[(f=s)+8>>2])break p;break n;case 10:break m;default:break C}if(p=0,M[r+4>>1]|M[MA>>1])break n;ee=1,f=cA;break p}C:switch(0|m){case 0:case 5:break C;default:break p}f=(l[f+2|0]==1?-32:0)+f|0}if(ce?(m=e[144464+(l[f+2|0]<<2)>>2],e[f+8>>2]=m):m=e[f+8>>2],T>>>0<=1791){if(p=1,e[e[144464+(I<<2)>>2]>>2]==e[m>>2])break n;if(!(!ee|l[m+11|0]!=2)){p=(0|I)==l[m+13|0];break n}p=(0|I)==l[m+12|0];break n}I=31&T,p=0;p:switch(T>>>5&7){case 0:p=(0|I)==l[m+11|0];break n;case 1:p=(0|I)==(15&M[m+6>>1]);break n;case 2:p=e[m+4>>2]>>>I&1;break n;case 4:break p;default:break n}p:switch(0|I){case 0:case 1:case 2:case 3:case 4:if(l[e[144464+(l[f+2|0]<<2)>>2]+11|0]!=2){if(l[e[144464+(l[f+34|0]<<2)>>2]+11|0]!=2)break n;f=f+32|0}m=15&l[f+3|0],m=!A|!(2&l[A+56|0])?m:l[f+6|0]<=m>>>0?4:m;C:{switch(I-3|0){case 1:p=l[f+6|0]<=m>>>0;break n;case 0:if(p=1,m>>>0<=3)break C;break n}if(p=1,e[102832+(I<<2)>>2]>(0|m))break n}p=0;break n;case 17:if(!l[m+11|0]){p=1;break n}p=(32&l[r+1|0])>>>5|0;break n;case 18:p=M[f+4>>1]!=0;break n;case 19:if(p=1,M[f+36>>1])break n;p=!l[e[f+40>>2]+11|0];break n;case 9:if(M[f+4>>1])break n;for(;;){if(p=!!(0|(m=12&l[f-29|0])),m)break n;if(M[4+(f=f-32|0)>>1])break}break n;case 10:p=l[m+11|0]!=2;break n;case 11:for(;;){if(p=!!(0|(m=M[f+36>>1])),m)break n;if(m=f,f=f+32|0,l[e[m+40>>2]+11|0]==2)break}break n;case 12:if(p=1,(254&l[m+11|0])==2)break n;p=(16&l[m+4|0])>>>4|0;break n;case 13:for(;p=(l[e[f+8>>2]+11|0]==2)+p|0,m=M[f+4>>1],f=f-32|0,!m;);p=(0|p)==1;break n;case 14:for(;p=(l[e[f+8>>2]+11|0]==2)+p|0,m=M[f+4>>1],f=f-32|0,!m;);p=(0|p)==2;break n;case 16:break p;default:break n}p=(16&l[0|f])>>>4|0}else if(p=0,(0|f)==15){p:switch(I-1|0){case 0:p=zA;break n;case 1:break p;default:break n}p=e[at+132>>2]!=0}n:if(1970>>>(f=(m=65535&t)>>>12|0)&1)f=g[f+102848|0];else{switch(0|f){case 0:if(f=1,(3840&m)!=3328)break n;f=1+(1+(255&m)>>>1|0)|0;break n;case 6:f=(m>>>9&7)-5>>>0<2?12:1;break n;case 2:case 3:f=(0|(f=3840&m))==3328||(0|f)==1536?2:1;break n}f=4,(m=M[a+4>>1])>>>0>61439||(f=(0|m)==2?3:2)}if(a=((f=M[(a=(f<<1)+a|0)>>1]==3)<<1)+a|0,f^=p,E=Qe?f|E:f&E,Qe=4096&t,(57344&(t=M[a>>1]))!=8192)break}if(!(1&E))if((63488&t)!=26624){n:if(1970>>>(f=t>>>12|0)&1)f=g[f+102848|0];else{switch(0|f){case 0:if(f=1,(3840&t)!=3328)break n;f=1+(1+(255&t)>>>1|0)|0;break n;case 6:f=(t>>>9&7)-5>>>0<2?12:1;break n;case 2:case 3:f=(0|(t&=3840))==3328||(0|t)==1536?2:1;break n}f=4,(t=M[a+4>>1])>>>0>61439||(f=(0|t)==2?3:2)}a=(((65024&M[(t=(f<<1)+a|0)>>1])==24576)<<1)+t|0}else a=((255&t)<<1)+a|0}f=a-2|0,m=L;break A}l:switch(f>>>1|0){case 0:a=(((255&t)<<1)+a|0)-2|0;break e;case 5:e[i>>2]=2|e[i>>2],((t=l[e[r+40>>2]+12|0])-28&255)>>>0<=5&&(f=M[4+(t=((t<<2)+a|0)-112|0)>>1],t=M[t+2>>1],e[i+96>>2]=t>>>4<<24>>24,e[i+76>>2]=(15&t)<<18|f<<2),a=a+24|0;break e;case 6:break l;default:break e}((t=l[e[et>>2]+13|0])-28&255)>>>0<=5&&(f=M[4+(t=((t<<2)+a|0)-112|0)>>1],t=M[t+2>>1],e[i+100>>2]=t>>>4<<24>>24,e[i+80>>2]=(15&t)<<18|f<<2),a=a+24|0;break e}t=M[(a=a+2|0)>>1]|t<<16&983040;u:switch(f-1|0){case 0:if((0|Z)>9)break e;e[(o+32|0)+(Z<<2)>>2]=a,a=(e[34459]+(t<<1)|0)-2|0,Z=Z+1|0;break e;case 1:e[i+124>>2]=t;break e;case 2:break u;default:break e}e[i+128>>2]=t;break e}e[108+(f=(((0|f)!=1)<<3)+i|0)>>2]=M[a+2>>1]|(255&t)<<16,t=M[a+4>>1]<<16,a=a+6|0,e[f+112>>2]=t|M[a>>1];break e}if(m=M[(f=a+2|0)>>1],T=t>>>4|0,e[88+(E=((I=p-11|0)<<2)+i|0)>>2]=255&T,e[E+68>>2]=t<<18&3932160|m<<2,(0|(a=M[a+4>>1]))==2){a=f;break e}if(m=t>>>0<=53247?a>>>0>61439?2:1:L-((0|I)==4)|0,p-13>>>0>1)break A;e[E+88>>2]=T<<24>>24;break A}e[o+4>>2]=v0(o+75|0,e[c>>2]),e[o>>2]=t,fr(ve,85851,o);break e}e[4+((f<<2)+i|0)>>2]=m,m=ce&&(0|f)==1?1:L}f=a}(0|m)!=1|(0|Z)<=0?(a=f,L=m):(a=e[(o+32|0)+((Z=Z-1|0)<<2)>>2],L=0)}if(a=a+2|0,(0|L)==1)break}!s|l[r+17|0]!=2||(A=e[r+4>>2],e[s>>2]=e[r>>2],e[s+4>>2]=A,A=e[r+28>>2],e[s+24>>2]=e[r+24>>2],e[s+28>>2]=A,A=e[r+20>>2],e[s+16>>2]=e[r+16>>2],e[s+20>>2]=A,A=e[r+12>>2],e[s+8>>2]=e[r+8>>2],e[s+12>>2]=A),g[r+23|0]=e[i+44>>2],(A=e[i+68>>2])?(e[r+24>>2]=A,A=i+88|0):(e[r+24>>2]=e[i+72>>2],A=i+92|0),e[r+28>>2]=e[A>>2]}J=o+80|0}function da(A,t,r,a,s,i){var o,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0;J=o=J-848|0;e:if(!(!e[A+104>>2]|8&l[s+2|0]|e[47202]==193)){for(e[a>>2]=0,e[33272]=0,e[33274]=i,g[o+192|0]=0,e[33273]=o+192;p=(i=p)+1|0,g[0|(he=t+i|0)]-48>>>0<10;);e[56798]=0,Ke=o+188|0,E=-2147483648,J=ce=J-16|0;A:if(c=l[0|t]){I=t;r:{for(;;){if(!((0|(c=c<<24>>24))==32|c-9>>>0<5))break r;if(c=l[I+1|0],I=I+1|0,!c)break}break A}r:switch((c=l[0|I])-43|0){case 0:case 2:break r;default:break A}Z=(0|c)==45?-1:0,I=I+1|0}else I=t;for(;c=-48,(((T=g[0|I])-48&255)>>>0<10||(c=-87,(T-97&255)>>>0<26||(c=-55,!((T-65&255)>>>0>25))))&&!((0|(T=c+T|0))>=10);)Kr(ce,10,0,0,0,L,ee,0,0),c=1,e[ce+8>>2]|e[ce+12>>2]||(Qe=It(L,ee,10,0),(0|(We=$))==-1&~T>>>0<Qe>>>0||(c=We,ee=(L=T+Qe|0)>>>0<T>>>0?c+1|0:c,ve=1,c=m)),I=I+1|0,m=c;Ke&&(e[Ke>>2]=ve?I:t);A:{r:{if(m)e[56798]=68,L=-2147483648,ee=0;else if(!ee&L>>>0<2147483648)break r;if(!Z){e[56798]=68,E=2147483647;break A}if(!(!ee&L>>>0<=2147483648)){e[56798]=68;break A}}E=(Z^L)-Z|0}if(J=ce+16|0,Z=E,!(e[56798]|e[o+188>>2]==(0|t))){A:{r:{s:{if(!(!((0|(T=64&l[A+109|0]?4:3))!=(0|i)|e[A+124>>2]!=g[t-2|0])&g[t-3|0]-48>>>0<10)){i:{if(e[A+124>>2]!=32){if(!(16&l[A+105|0]))break s;if((0|i)==3)break i;break s}if((0|i)!=3)break s}if(4&l[s+2|0]|g[t-2|0]-48>>>0>=10)break s}g[133104]=0,g[o+288|0]=0,rA=1;break r}if(g[133104]=0,e[33275]=0,g[o+288|0]=0,cA=1,ve=0,l[0|t]==48)break A}ve=fa(A,t,he,s,0)}l[0|he]!=46|g[t+p|0]-48>>>0<10|1&g[s+13|0]|g[he+2|0]-48>>>0<10||(g[0|he]=0);A:if(!ve||(Qe=1,e[A+212>>2]==26741)){if(E=o+256|0,e[o+844>>2]=E,f=p,64&l[s+1|0]&&(g[o+256|0]=45,E=o+256|1,e[o+844>>2]=E,f=i+2|0),I=l[t+f|0]){for(;!((255&I)==32|(0|f)>28)&&(c=e[o+844>>2],e[o+844>>2]=c+1,g[0|c]=I,I=l[(f=f+1|0)+t|0]););E=e[o+844>>2]}if(g[0|E]=0,Qe=1,c=g[o+256|0]){if(!(m=e[A+136>>2])||vr(o+256|0,m)){if(c-48>>>0<10||(e[o+176>>2]=o+256,DA(c=o+800|0,88653,o+176|0),!RA(A,c,133104)))break A;e[a>>2]=128|e[a>>2],e[o+160>>2]=o+256,DA(c=o+800|0,88773,o+160|0),RA(A,c,133116),Qe=0}ve=2}}Ke=e[s>>2],g[o+352|0]=0,g[o+624|0]=0;A:{r:{s:if(!(!cA|l[0|t]!=48||(0|(c=g[t+1|0]))==32|(0|c)==e[A+128>>2])){i:{if((0|i)==2){if(l[t+3|0]!=58|g[t+5|0]-48>>>0>=10||!((0|(c=g[t+7|0]))==32|c-9>>>0<5))break i;break s}if((0|i)>3)break r}if(l[0|t]==48&&!((0|(c=i-1|0))<=0))for(f=0;;){if(RA(A,88875,HA(m=o+288|0)+m|0),l[(f=f+1|0)+t|0]!=48)break s;if(!((0|c)>(0|f)))break}}s:{i:{if(!((0|(c=g[0|he]))==32&&16&l[A+105|0])){if(We=2,L=i+2|0,(0|c)==e[A+124>>2])break i;ee=1,E=0,c=0;break s}We=1,L=i+2|0}if(4&l[s+14|0])E=1,c=0,ee=1;else for(c=0,I=1,f=i,ee=1;;){m=c,c=I,I=(ce=f+We|0)+t|0,f=0;i:{for(;;){if(E=1,!(g[f+I|0]-48>>>0>=10)){if((0|T)!=(0|(f=f+1|0)))continue;break i}break}c=m;break s}if(g[I+T|0]-48>>>0<10){c=m;break s}if(f=0,g[I-1|0]-48>>>0<10){c=m;break s}i:{for(;;){if(l[(f+ce|0)+t|0]==48){if((0|T)!=(0|(f=f+1|0)))continue;break i}break}ee=0}if((0|(m=g[(f=T+ce|0)+t|0]))!=e[A+124>>2]&(!(16&l[A+105|0])|(0|m)!=32))break s;if(L=f+2|0,4&l[2+(z(I=c+1|0,12)+s|0)|0])break}}f=!Z;s:if(!(!ee|!(64&l[1+(z(c,12)+s|0)|0])|e[A+212>>2]!=26741)){i:switch(l[0|(m=t+L|0)]-97|0){case 0:case 4:break i;default:break s}i:{c:{o:{u:switch((I=l[m+1|0])-116|0){case 6:break s;case 1:case 2:case 3:case 4:case 5:break c;case 0:break u;default:break o}if(l[m+2|0]!=116)break i;break s}if((0|I)==32)break s}if(!(!!((0|Z)%1e3|0)&(0|c)!=1)&&(0|I)==108)break s}e[33274]=1|e[33274]}L=32768&Ke,f&=rA;s:if(e[A+128>>2]!=g[0|he]|g[t+p|0]-48>>>0>=10){i:{if(!f){if(f=0,m=1,!((0|c)>0&E))break i;Z=(p=ks(A,Z,c,ee,o+624|0))?0:Z,f=!!(0|p),I=0;break s}f=1,Z=0,e[33275]==1&&(e[o+144>>2]=c+1,DA(p=o+800|0,89026,o+144|0),RA(A,p,o+688|0)||(e[o+128>>2]=c,DA(p=o+800|0,89026,o+128|0),RA(A,p,o+624|0)))}m=1,I=0}else RA(A,88882,o+624|0),m=0,I=256;E=L?2:ve;s:{if(c|l[o+624|0]|l[0|he]!=46){if(c)break s}else RA(A,89192,o+624|0);if(e[o+844>>2]=t,g[t+1|0]-48>>>0<10)for(;p=e[o+844>>2],e[o+844>>2]=p+1,g[p+2|0]-48>>>0<10;);if(g[e[o+844>>2]-1|0]-48>>>0>=10||(e[o+416>>2]=e[o+844>>2]-1,kr(A,o+416|0,o+192|0,a,4,s)&&(e[33272]=2)),l[o+192|0]|l[e[o+844>>2]]==48||kr(A,o+844|0,o+192|0,a,4,s)&&(e[33272]=1),cA){if(!E&m&&(e[o+112>>2]=Z,DA(s=o+800|0,89214,o+112|0),RA(A,s,r)))break A;if(1&g[A+110|0]){for(p=t;(32|l[0|p])!=32;)p=p+1|0;e[o+416>>2]=p,l[p+1|0]==37&&(RA(A,89328,r),s=HA(r),g[e[o+416>>2]+1|0]=32,r=r+s|0)}}}gs(A,Z,o+416|0,f,c,E|I|rA),!(2&l[A+109|0])|(0|c)<=0?(e[o+60>>2]=15,e[o- -64>>2]=o+624,e[o+56>>2]=o+416,e[o+52>>2]=o+352,e[o+48>>2]=o+288,DA(r,89415,o+48|0)):(e[o+88>>2]=15,e[o+96>>2]=o+416,e[o+92>>2]=o+352,e[o+84>>2]=o+624,e[o+80>>2]=o+288,DA(r,89346,o+80|0));s:if(!m)for(;;){for(i=i+1|0,m=0;m=(p=m)+1|0,g[(c=i+p|0)+t|0]-48>>>0<10;);s=2;i:{c:{o:{u:{l:{n:switch((m=57344&e[A+104>>2])-8192>>>13|0){case 6:break c;case 2:break o;case 0:case 4:case 5:break u;case 1:break l;case 3:break n;default:break i}s=5}if((0|(m=l[0|(f=t+i|0)]))==48)for(;RA(A,88875,c=o+688|0),sa(r,c),p=p-1|0,(0|(m=l[0|(f=(i=i+1|0)+t|0)]))==48;);if((0|s)<(0|p)|(m<<24>>24)-48>>>0>=10)break i;s=o+688|0,gs(A,Vs(f),s,0,0,0),sa(r,s),i=i+p|0;break i}if(gs(A,Vs(s=t+i|0),o+416|0,0,0,0),!((0|m)==8192&l[0|s]!=48)){if(e[o+16>>2]=p,DA(s=o+800|0,89508,o+16|0),!RA(A,s,o+688|0))break i;sa((0|m)==49152?r:o+416|0,o+688|0)}sa(r,o+416|0),i=c;break i}if((0|p)>4||l[0|(s=t+i|0)]==48)break i;gs(A,i=Vs(s),s=o+688|0,0,0,0),sa(r,s),i=c;break i}if(!((0|p)<=1))for(;;){if(e[o+32>>2]=g[t+i|0],DA(s=o+800|0,89575,o+32|0),!RA(A,s,o+688|0))break i;if(sa(r,o+688|0),i=i+1|0,!((0|(p=p-1|0))>1))break}}i:if(!((m=l[0|(p=t+i|0)])-48>>>0>=10||HA(r)>>>0>=190))for(;;){if(s=o+688|0,Za(A,g[0|p]-48|0,0,2,s),c=HA(r),e[o>>2]=15,e[o+4>>2]=s,DA(r+c|0,89594,o),(m=l[0|(p=(i=i+1|0)+t|0)])-48>>>0>=10)break i;if(!(HA(r)>>>0<=189))break}if(RA(A,89678,o+688|0)&&sa(r,o+688|0),e[A+128>>2]!=(0|m)|g[1+(t+i|0)|0]-48>>>0>=10)break s;RA(A,88882,s=o+688|0),sa(r,s)}if(!(s=l[0|r])|(0|s)==21||(s=Gt(o+184|0,t=1+(t+i|0)|0),i=e[o+184>>2],!(2&l[A+106|0])|(0|i)!=32||(Gt(o+184|0,t+s|0),i=e[o+184>>2]),is(i)|ee||(A=HA(r)+r|0,g[0|A]=11,g[A+1|0]=0)),e[a>>2]=-2147483648|e[a>>2],e[33275]=e[33275]-1,f=1,Qe)break e;e[33264]=1;break e}e[a>>2]=-129&e[a>>2],f=0;break e}f=1}}return J=o+848|0,f}function bt(A,t,r,a){var s,i,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0,MA=0,zA=0;if(J=s=J-416|0,ve=M[r+8>>1],L=l[r+10|0],64&(i=e[r>>2])){for(e[r>>2]=-65&i,g[199388]=1,f=e[47202],E=e[49846],p=e[47352],I=e[47351],m=e[47350];;){o=(c=e[198304+(I<<2)>>2])>>8;e:{A:switch((31&c)-9|0){case 0:f=o;break e;case 4:E=o;break e;case 3:break A;default:break e}p=c>>>0>=256?o+p|0:0}if(!(!(128&c)&(0|m)>(0|(I=I+1|0))))break}e[47352]=p,e[47351]=I,e[49846]=E,e[47202]=f}E=0;e:if(!((0|(o=e[49572]))>997)){if(1048576&i||!l[0|t])l[199388]&&(e[49572]=o+1,g[199388]=0,e[(A=190288+(o<<3)|0)>>2]=983042,e[A+4>>2]=0),g[189088]=0;else if(!((0|o)>990)){(0|(o=e[A+8224>>2]))>0&&(e[A+8224>>2]=o-1),f=512&i?a:a+4|0,p=(240&e[47202])==16,(ce=2&i)&&((0|(c=e[47200]))<3||(0|(o=e[47350]))>243||(l[199388]&&(e[(I=198300+(o<<2)|0)>>2]=-129&e[I>>2]),e[47350]=o+1,g[199388]=1,Qe=(0|c)==3?20:c,e[198304+(o<<2)>>2]=Qe<<8|193)),m=p?f:a;A:{r:{if(!(8&i)){for(;a=l[(o=T)+t|0],g[o+(s+240|0)|0]=a,223&a&&(T=o+1|0,o>>>0<160););if(g[s+66|0]=0,T=s- -64|2,E=NA(e[47192],t,r,T),e[s+412>>2]=E,!(4096&E))break r;wt(t,s+240|0,o);break e}s:if(xa(t,87276,3))Ns(t,189088,s+240|0);else{if(I=0,223&(o=l[0|(T=t+3|0)]))for(;g[(s+240|0)+I|0]=Ht(o<<24>>24),I=I+1|0,223&(o=l[0|(T=T+1|0)]););g[(a=s+240|0)+I|0]=0,o=0;i:if(!((0|(r=e[34461]))<=0)){for(;;){if(!vr(a,z(o,44)+137856|0)){e[34457]=o;break i}if((0|r)==(0|(o=o+1|0)))break}o=r}if((0|(r=(0|r)==(0|o)?-1:o))<=0)break s;ha(r),g[189090]=0,g[189089]=r,g[189088]=21}E=-2147483648,e[s+412>>2]=-2147483648,a=-1;break A}if(!(!(8388608&E)|8&l[r+12|0])){for(J=p=J-208|0,I=e[A+60>>2],c=t;a=c,c=c+1|0,l[0|a]!=32;);Gt(p+204|0,c);r:if(is(e[p+204>>2])){f=qA(p,189088),c=(!(256&I)|(ee=TA(A,c,r+12|0,0))>>>15)&!(67108864&ee)&l[189088]!=21;s:{i:{if(512&I){if(!(c&!(16&l[r+12|0])))break i;break s}if(c)break s}qA(189088,f);break r}if(g[0|a]=45,e[r>>2]=-2&e[r>>2],E=0,c=189088,Z=TA(e[47192],t,r,0),e[s+412>>2]=Z,!((0|I)<=0)&&(f=l[189088])){for(;E=(l[e[144464+(f<<2)>>2]+11|0]==2)+E|0,f=l[0|(c=c+1|0)];);if(!((31&I)>=(0|E))){g[0|a]=32,e[s+412>>2]=TA(e[47192],t,r,0);break r}}e[s+412>>2]=128|(Z||ee),e[33264]=1}J=p+208|0}if(a=-1,l[189088]==21){if(I=qA(s+16|0,132848),p=e[s+412>>2],c=wt(t,s+240|0,o),f=s- -64|1,(0|(a=wn(l[189089]?189089:87315,188772,189296)))<0||(e[r>>2]=4194304|e[r>>2],l[s+66|0]?(b[s+64>>1]=8192,p=TA(e[47193],f,r,0)):p=NA(e[47193],c,r,T)),l[189088]==21&&(o=wt(c,s+240|0,o),(0|(a=wn(l[189089]?189089:87315,188772,189296)))<0||(e[r>>2]=4194304|e[r>>2],l[s+66|0]?(b[s+64>>1]=8192,p=TA(e[47193],f,r,0)):p=NA(e[47193],o,r,T)),E=4096,l[189088]==21))break e;e[s+412>>2]=p,(0|a)>=0||(g[189090]=0,b[94544]=3341,(0|a)==-1&&(qA(132848,I),ha(e[e[32972]+60>>2]),a=e[e[32972]+60>>2]))}E=e[s+412>>2],128&i||(m=268435456&E&&(0|m)<=1?1:m,!(256&E)|528&i|e[A+8224>>2]|2&l[r-11|0]||(e[A+8224>>2]=3,m=(0|m)<=4?4:m)),m=(0|m)<=0&&e[49846]>2?1:m}if(r=l[199388],!((0|m)<=0|(0|(T=e[49572]))>990)){e[49572]=T+1,c=1&r,r=0,b[(o=190288+(T<<3)|0)>>1]=c?2:0,g[o+7|0]=0,g[o+3|0]=0,c=m>>>0>1,g[o+2|0]=c?9:11,b[o+4>>1]=0,e[A+8236>>2]=0;A:if(!(!(o=c?m-2|0:0)|(0|(T=e[49572]))>990))for(f=A+8236|0;;){if(e[49572]=T+1,b[(c=190288+(T<<3)|0)>>1]=0,g[c+7|0]=0,g[c+3|0]=0,p=o>>>0>1,g[c+2|0]=p?9:11,b[c+4>>1]=0,e[f>>2]=0,T=e[49572],(0|(o=p?o-2|0:0))<=0)break A;if(!((0|T)<991))break}e[A+8228>>2]=0,e[A+8232>>2]=0}g[199388]=1&r,!ce|e[47200]!=1||(e[49572]=T+2,g[199388]=0,b[(o=190288+(T<<3)|0)>>1]=1&r?2:0,g[o+7|0]=0,b[o+2>>1]=10,b[o+4>>1]=0,b[o+12>>1]=0,e[o+8>>2]=1179648,g[o+15|0]=0,1&i&&rr(g[t+1|0])&&(r=l[199388],g[199388]=0,t=e[49572],e[49572]=t+2,b[(t=190288+(t<<3)|0)>>1]=r?2:0,g[t+7|0]=0,b[t+2>>1]=10,b[t+4>>1]=0,b[t+12>>1]=0,e[t+8>>2]=1179648,g[t+15|0]=0)),o=L>>>0<31;A:if(!((0|a)<0))if(t=e[49572],r=l[190290+((m=t-1|0)<<3)|0],l[189088]!=9|l[189089]!=21)(0|r)!=21&&(c=l[199388],g[199388]=0,g[7+(r=190288+(t<<3)|0)|0]=0,b[r+2>>1]=21,b[r+4>>1]=0,b[r>>1]=c?2:0,m=t),e[49572]=m+1,g[190295+(m<<3)|0]=a;else{if((0|r)!=21)break A;e[49572]=m}t=2047&ve,r=(o?L:31)<<11,p=(We=128&i)?l[e[144464+(l[189088]<<2)>>2]+11|0]?189088:189089:189088,(m=l[0|p])|!(1&g[199388])||(m=23,g[0|p]=23,g[p+1|0]=0),Ke=t|r,f=e[49572];A:if(m)if((0|f)>994)t=0;else for(MA=((-1610612736&E)==-2147483648)<<4,ve=Ke+1|0,zA=A+8233|0,t=0,c=1,ee=0,rA=1,L=-1,ce=-1,o=0;;){I=p+1|0;r:{if((0|(Z=255&m))!=255){if(r=e[144464+(Z<<2)>>2])break r;e[s>>2]=Z,J=r=J-16|0,e[r+12>>2]=s,Ds(132552,87474,s),J=r+16|0,f=e[49572]}if(!(m=l[0|I]))break A;if(p=I,(0|f)<995)continue;break A}r:if((0|(he=255&m))!=21)if((0|(p=l[r+11|0]))!=1){switch(r=ve,he-12|0){case 8:g[3+(r=190288+((L=f-1|0)<<3)|0)|0]=c,b[r>>1]=4|M[r>>1],r=o;break r;case 0:b[(r=190280+(f<<3)|0)>>1]=8|M[r>>1],r=o;break r;case 10:E|=16384,e[s+412>>2]=E,r=o;break r;case 3:break r}Z=l[199388],g[199388]=0,g[7+(r=190288+(he=f<<3)|0)|0]=0,g[r+2|0]=m,b[r+4>>1]=o,o=(Z?2:0)|MA,b[r>>1]=o,(0|p)==2?((0|c)>=4&&(g[189076]=1),(0|L)<0||(0|(t=f-1|0))!=(0|L)&&(g[190291+(t<<3)|0]=c),b[r>>1]=4|o,cA=(t=(0|c)>(0|ce))?f:cA,ce=t?c:ce,o=1,ee&&(g[r+7|0]=ee),L=f,ee=0,t=c):(!rA|!(64&l[0|zA])||(b[r>>1]=8|o),o=c),f=f+1|0,e[49572]=f,g[he+190291|0]=t,r=0,rA=0,c=o}else{if(!M[r+8>>1]){c=l[r+14|0],r=o;break r}if((0|L)<0){r=o,ee=Z;break r}g[190295+(L<<3)|0]=m,r=o}else e[(r=190288+(f<<3)|0)>>2]=1376256,b[r+4>>1]=0,g[r+7|0]=l[p+1|0],e[49572]=f+1,ha(l[p+1|0]),I=p+2|0,f=e[49572],r=o;if(!(m=l[0|I]))break A;if(p=I,o=r,!((0|f)<995))break}else t=0;131072&i&&(r=f+1|0,e[49572]=r,c=l[199388],g[199388]=0,g[7+(o=190288+(f<<3)|0)|0]=0,b[o+2>>1]=27,b[o+4>>1]=0,b[o>>1]=c?2:0,f=r),We||(b[190292+(T<<3)>>1]=Ke),e[A+8228>>2]=0,l[e[144464+(l[190282+(f<<3)|0]<<2)>>2]+11|0]!=2|(0|t)<4||(e[A+8228>>2]=1),(0|a)>=0&&(qA(132848,s+16|0),ha(e[e[32972]+60>>2]),r=l[199388],g[199388]=0,a=e[49572],b[(t=190288+(a<<3)|0)>>1]=r?2:0,b[t+2>>1]=21,b[t+4>>1]=0,g[t+7|0]=e[e[32972]+60>>2],f=a+1|0,e[49572]=f),(0|Qe)>0&&(g[199388]=0,e[49572]=f+1,e[(t=190288+(f<<3)|0)>>2]=655362,r=e[47350],e[47350]=r+1,g[t+7|0]=0,b[t+4>>1]=0,e[198304+(r<<2)>>2]=Qe<<8|225),1024&E&&(b[(t=190288+(cA<<3)|0)>>1]=64|M[t>>1]),e[A+8232>>2]=E}}return J=s+416|0,E}function Ka(A,t,r,a,s){var i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0,MA=0,zA=0,et=0,at=0;e:if(t){if(ce=s<<4,o=(t=e[33268])+r|0,!((0|t)<=0||(i=l[0|(f=ce+129360|0)],I=l[f+1|0]-i<<8,I=(0|t)!=1?(0|I)/(0|t)|0:I,(0|r)>=(0|o))))for(E=I>>>0>255,Z=I>>>8|0,i<<=8,f=0-I>>>8|0,Qe=(0|I)<=0,t=r;Qe?(m=(p=(p=(0|(c=(0|(c=e[101024+(g[z(t,6)+A|0]<<2)>>2]))<(0|f)?f:c))>=18?18:c)+(c=(0|(c=(0|(i=i+I|0))/256|0))>0?c:0)|0)>>>0>=254?254:p,c=c>>>0>=254?254:c):(c=z(t,6)+A|0,g[c+2|0]=E|l[c+2|0],c=(p=(0|(c=(0|i)/256|0))>0?c:0)>>>0>=254?254:p,m=(p=p+Z|0)>>>0>=254?254:p,i=i+I|0),p=z(t,6)+A|0,g[p+5|0]=c,g[p+4|0]=m,(0|o)!=(0|(t=t+1|0)););if(2&l[188785]?(f=e[33271],e[33270]=f):f=e[33270],Z=(E=l[3+(i=129360+(t=s<<4)|0)|0])-(c=l[i+2|0])<<8,Qe=e[i+4>>2],(0|r)>0?(Ke=101056,he=5,p=(0|Z)/(l[t+129368|0]-1|0)|0):(Ke=e[12+(t=t+129360|0)>>2],he=l[t+10|0],p=0),(0|o)<(0|f)){for(I=(0|r)<=0,ee=((t=Z>>31)^Z)-t|0,T=129360+(s<<4)|0,c<<=8,We=E<<8,E=0;;){A:{if(m=z(o,6)+A|0,!(!(1&I)&(0|(i=g[0|m]))<4)){r=0,t=o;r:if(1&((0|i)==5|I)){for(;(0|(p=g[z(t,6)+A|0]))<=6&&(r=((0|p)>3)+r|0,(0|f)!=(0|(t=t+1|0))););if(L=0,(0|(ve=(0|(t=l[T+8|0]))>(0|r)?r:t))<2){p=0,E=c;break r}p=(0|Z)/(ve-1|0)|0,E=c}else(0|ve)>0?E=p+E|0:(E=We+(z(ee,g[L+Ke|0])>>6)|0,(0|he)>(0|(L=L+1|0))||(Ke=e[T+12>>2],L=0));if(ve=ve-1|0,!((0|i)<4)){g[0|m]=6,r=(t=(0|(t=(0|E)/256|0))>0?t:0)+(((r=(i=e[Qe+(i<<2)>>2])>>31)^i)-r|0)|0;break A}}(0|i)!=3?(t=(0|E)/256|0,(63&l[m-6|0])>>>0>=3?(r=(i=e[Qe+(i<<2)>>2])>>31,r=(t=(0|(t=t-g[T+9|0]|0))>0?t:0)+((r^i)-r|0)|0):r=(t=(0|t)>0?t:0)+(((r=(i=e[Qe+(i<<2)>>2])>>31)^i)-r|0)|0):r=(t=(0|(t=(0|E)/256|0))>0?t:0)+(((r=(i=e[Qe+12>>2])>>31)^i)-r|0)|0}if(g[m+5|0]=t>>>0>=254?254:t,I=0,t=(0|r)>0?r:0,g[m+4|0]=t>>>0>=254?254:t,g[m+2|0]=l[m+2|0]|i>>>31,(0|f)==(0|(o=o+1|0)))break}o=f}if(!(l[133068]||((268435455&s)==3&&(t=z(o,6)+A|0,g[t+2|0]=2|l[t+2|0]),e[33269]?(t=l[5+(i=100768+(s<<4)|0)|0],c=i+3|0,r=l[i+4|0]-t|0):(t=l[2+(r=100768+(s<<4)|0)|0],c=ce+100768|0,r=l[r+1|0]-t|0),i=z(o,6)+A|0,g[i+5|0]=t>>>0>=254?254:t,g[i+2|0]=l[i+2|0]|r>>>31,t=(p=t)+(((t=r>>31)^r)-t|0)|0,g[i+4|0]=t>>>0>=254?254:t,r=z(f,6)+A|0,g[r+1|0]=l[0|c],t=o+1|0,l[0|r]==4&&(g[0|r]=6),(0|(r=a-t|0))<=0||(s=l[12+(o=100768+(s<<4)|0)|0],o=l[o+13|0]-s<<8,o=(0|r)!=1?(0|o)/(0|r)|0:o,(0|t)>=(0|a)))))for(p=o>>>0>255,I=o>>>8|0,r=s<<8,s=0-o>>>8|0,E=(0|o)<=0;E?(c=(f=(c=(0|(i=(0|s)>(0|(i=e[101024+(g[z(t,6)+A|0]<<2)>>2]))?s:i))>=18?18:i)+(i=(0|(i=(0|(r=r+o|0))/256|0))>0?i:0)|0)>>>0>=254?254:f,f=i>>>0>=254?254:i):(i=z(t,6)+A|0,g[i+2|0]=p|l[i+2|0],f=(i=(0|(i=(0|r)/256|0))>0?i:0)>>>0>=254?254:i,c=(i=i+I|0)>>>0>=254?254:i,r=r+o|0),i=z(t,6)+A|0,g[i+5|0]=f,g[i+4|0]=c,(0|a)!=(0|(t=t+1|0)););}else{if(t=(he=e[34455])+z(s,68)|0,o=l[t+24|0],f=l[t+25|0],i=(t=e[33268])+r|0,!((0|t)<=0||(m=f-o<<8,m=(0|t)!=1?(0|m)/(0|t)|0:m,(0|r)>=(0|i))))for(p=m>>>0>255,I=m>>>8|0,t=o<<8,f=0-m>>>8|0,E=(0|m)<=0;E?(o=(o=(Z=(0|(o=(0|(o=e[101024+(g[z(r,6)+A|0]<<2)>>2]))<(0|f)?f:o))>=18?18:o)+(c=(0|(o=(0|(t=t+m|0))/256|0))>0?o:0)|0)>>>0>=254?254:o,c=c>>>0>=254?254:c):(o=z(r,6)+A|0,g[o+2|0]=p|l[o+2|0],c=(o=(0|(o=(0|t)/256|0))>0?o:0)>>>0>=254?254:o,o=(o=o+I|0)>>>0>=254?254:o,t=t+m|0),Z=z(r,6)+A|0,g[Z+5|0]=c,g[Z+4|0]=o,(0|i)!=(0|(r=r+1|0)););2&l[188785]?(r=e[33271],e[33270]=r):r=e[33270],f=r,t=he+z(s,68)|0;A:if((0|(I=l[t+33|0]))!=255)for(;;){if((0|(f=f-1|0))<(0|i)){f=r;break A}if(!(g[z(f,6)+A|0]<4))break}if((0|r)>(0|i)){for(zA=((o=(cA=(E=l[t+32|0])-(m=l[0|(c=t+31|0)])<<8)>>31)^cA)-o|0,We=(0|(t=l[0|(p=t+30|0)]))==255,Z=I<<8,Qe=m<<8,et=E<<8,ce=he+z(s,68)|0,at=(0|t)!=255,E=0,I=0,t=1,m=0;;){A:{if(T=z(i,6)+A|0,!(!(1&t)&(0|(ee=g[0|T]))<4)){r:if(1&((0|ee)==5|t)){s:if(at){if(rA=1,t=0,o=p,!((0|f)<=(0|(m=i+1|0)))){for(;;){if(o=p,(0|(I=g[z(m,6)+A|0]))>6)break s;if(t=((0|I)>3)+t|0,(0|f)==(0|(m=m+1|0)))break}o=p}}else{if(t=0,m=i,o=c,(0|f)<=(0|i))break s;for(;;){if(o=c,(0|(I=g[z(m,6)+A|0]))>6)break s;if(t=((0|I)>3)+t|0,(0|f)==(0|(m=m+1|0)))break}o=c}if(I=l[0|o]<<8,MA=0,(0|(m=(0|t)<(0|(o=l[ce+34|0]))?t:o))<2){Ke=0;break r}Ke=(0|cA)/(m-1|0)|0}else(0|i)!=(0|f)?rA?(We=1,m=m+1|0,rA=0,I=Qe):(0|m)>0?(I=I+Ke|0,rA=0):(rA=0,I=(z(g[16+(ce+MA|0)|0],zA)>>6)+et|0,MA=(0|(t=MA+1|0))<l[ce+35|0]?t:0):(We=2,I=Z);if(m=m-1|0,!((0|ee)<4)){t=o=i+1|0;r:if(!((0|r)<=(0|i)))for(;;){if(g[z(t,6)+A|0]>1)break r;if(!((0|r)>=(0|(t=t+1|0))))break}g[0|T]=6,g[T+1|0]=l[ce+26|0],E=0,L=l[ce+27|0],i=(0|(i=(0|I)/256|0))>0?i:0,g[T+5|0]=i>>>0>=254?254:i,i=i+L|0,g[T+4|0]=i>>>0>=254?254:i,ve=t-o|0;break A}}if((0|ee)>=2){t=o=i+1|0;r:if(!((0|r)<=(0|i)))for(;;){if(g[z(t,6)+A|0]>1)break r;if(!((0|r)>=(0|(t=t+1|0))))break}E=0,i=(0|(i=(0|I)/256|0))>0?i:0,g[T+5|0]=i>>>0>=254?254:i,L=e[100976+(ee<<2)>>2],g[T+2|0]=l[T+2|0]|L>>>31,i=(0|(i=(ee=i)+(((i=L>>31)^L)-i|0)|0))>0?i:0,g[T+4|0]=i>>>0>=254?254:i,ve=t-o|0}else(0|ve)<=1?(L=g[36+(ce+We|0)|0],t=0):(L=g[36+(t=ce+We|0)|0],t=(g[t+39|0]-L|0)/(ve-1|0)|0),o=e[100976+(ee<<2)>>2],g[T+2|0]=l[T+2|0]|o>>>31,t=(0|(t=(((0|I)/256|0)+L|0)+z(t,E)|0))>0?t:0,g[T+5|0]=t>>>0>=254?254:t,t=(0|(t=t+(((ee=o)^(o>>=31))-o|0)|0))>0?t:0,g[T+4|0]=t>>>0>=254?254:t,E=E+1|0,o=i+1|0}if(t=0,(0|r)==(0|(i=o)))break}i=r}if(l[133068]||(o=z(i,6)+A|0,e[33269]?(c=he+z(s,68)|0,t=l[c+47|0],f=l[c+46|0]-t|0,c=c+45|0):(c=he+z(s,68)|0,t=l[c+44|0],f=l[c+43|0]-t|0,c=c+42|0),c=l[0|c],p=(((p=f>>31)^f)-p|0)+t|0,g[o+4|0]=p>>>0>=254?254:p,g[o+5|0]=t>>>0>=254?254:t,t=z(i,6)+A|0,g[t+2|0]=l[t+2|0]|f>>>31,r=z(r,6)+A|0,g[r+1|0]=c,t=i+1|0,l[0|r]==4&&(g[0|r]=6),(0|(r=a-t|0))<=0)||(o=he+z(s,68)|0,s=l[o+48|0],o=l[o+49|0]-s<<8,o=(0|r)!=1?(0|o)/(0|r)|0:o,(0|t)>=(0|a)))break e;for(p=o>>>0>255,I=o>>>8|0,r=s<<8,s=0-o>>>8|0,E=(0|o)<=0;E?(c=(f=(c=(0|(i=(0|s)>(0|(i=e[101024+(g[z(t,6)+A|0]<<2)>>2]))?s:i))>=18?18:i)+(i=(0|(i=(0|(r=r+o|0))/256|0))>0?i:0)|0)>>>0>=254?254:f,f=i>>>0>=254?254:i):(i=z(t,6)+A|0,g[i+2|0]=p|l[i+2|0],f=(i=(0|(i=(0|r)/256|0))>0?i:0)>>>0>=254?254:i,c=(i=i+I|0)>>>0>=254?254:i,r=r+o|0),i=z(t,6)+A|0,g[i+5|0]=f,g[i+4|0]=c,(0|a)!=(0|(t=t+1|0)););}}function Oa(){var A,t=0,r=0,a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0;if((0|(A=e[36455]))!=(0|(f=e[36454]))){e:if(!((0|(T=e[36427]))<0|(0|f)==(0|T))){r=I=e[8+(216192+((m=T)<<4)|0)>>2];A:{for(;;){if((a=e[(o=216192+((m=(0|(a=m-1|0))<0?169:a)<<4)|0)>>2])-5>>>0<2)break A;r:{if((0|a)<=4){if(e[o+12>>2]!=(0|r))break A;if(a=M[o+4>>1],e[o+12>>2]=I,r=e[o+8>>2],16&(t=M[r>>1]))break r;for(Z=32&t?(z(a,12)>>>0)/10|0:a,s=0,a=r,c=0;;){t=M[r>>1];s:if(!(c>>>0<3&&8&t)){if(t=t<<16>>16,(0|(L=(p=b[2+((E=c<<1)+r|0)>>1])-(i=b[2+(E=I+E|0)>>1])|0))>(0|(p=(0|z(Z,(0|z(e[200944+(c<<2)>>2],(0|L)>0?p+(i<<1)|0:(p<<1)+i|0))/3e3|0))/256|0)))s||((0|t)<0?a=r:(a=0,t=(0|(t=e[44469]+1|0))<=169?t:0,e[44469]=t,(t=177888+(s=t<<6)|0)&&(a=M[r+4>>1]|M[r+6>>1]<<16,i=M[r>>1]|M[r+2>>1]<<16,b[t>>1]=i,b[t+2>>1]=i>>>16,b[t+4>>1]=a,b[t+6>>1]=a>>>16,a=M[r+60>>1]|M[r+62>>1]<<16,i=M[r+56>>1]|M[r+58>>1]<<16,b[t+56>>1]=i,b[t+58>>1]=i>>>16,b[t+60>>1]=a,b[t+62>>1]=a>>>16,a=M[r+52>>1]|M[r+54>>1]<<16,i=M[r+48>>1]|M[r+50>>1]<<16,b[t+48>>1]=i,b[t+50>>1]=i>>>16,b[t+52>>1]=a,b[t+54>>1]=a>>>16,a=M[r+44>>1]|M[r+46>>1]<<16,i=M[r+40>>1]|M[r+42>>1]<<16,b[t+40>>1]=i,b[t+42>>1]=i>>>16,b[t+44>>1]=a,b[t+46>>1]=a>>>16,a=M[r+36>>1]|M[r+38>>1]<<16,i=M[r+32>>1]|M[r+34>>1]<<16,b[t+32>>1]=i,b[t+34>>1]=i>>>16,b[t+36>>1]=a,b[t+38>>1]=a>>>16,a=M[r+28>>1]|M[r+30>>1]<<16,i=M[r+24>>1]|M[r+26>>1]<<16,b[t+24>>1]=i,b[t+26>>1]=i>>>16,b[t+28>>1]=a,b[t+30>>1]=a>>>16,a=M[r+20>>1]|M[r+22>>1]<<16,i=M[r+16>>1]|M[r+18>>1]<<16,b[t+16>>1]=i,b[t+18>>1]=i>>>16,b[t+20>>1]=a,b[t+22>>1]=a>>>16,a=M[r+12>>1]|M[r+14>>1]<<16,i=M[r+8>>1]|M[r+10>>1]<<16,b[t+8>>1]=i,b[t+10>>1]=i>>>16,b[t+12>>1]=a,b[t+14>>1]=a>>>16,g[s+177904|0]=0,b[t>>1]=32768|M[t>>1],a=t))),t=p+M[E+2>>1]|0;else{if((0-p|0)<=(0|L))break s;s||((0|t)<0?a=r:(a=0,t=(0|(t=e[44469]+1|0))<=169?t:0,e[44469]=t,(t=177888+(s=t<<6)|0)&&(a=M[r+4>>1]|M[r+6>>1]<<16,i=M[r>>1]|M[r+2>>1]<<16,b[t>>1]=i,b[t+2>>1]=i>>>16,b[t+4>>1]=a,b[t+6>>1]=a>>>16,a=M[r+60>>1]|M[r+62>>1]<<16,i=M[r+56>>1]|M[r+58>>1]<<16,b[t+56>>1]=i,b[t+58>>1]=i>>>16,b[t+60>>1]=a,b[t+62>>1]=a>>>16,a=M[r+52>>1]|M[r+54>>1]<<16,i=M[r+48>>1]|M[r+50>>1]<<16,b[t+48>>1]=i,b[t+50>>1]=i>>>16,b[t+52>>1]=a,b[t+54>>1]=a>>>16,a=M[r+44>>1]|M[r+46>>1]<<16,i=M[r+40>>1]|M[r+42>>1]<<16,b[t+40>>1]=i,b[t+42>>1]=i>>>16,b[t+44>>1]=a,b[t+46>>1]=a>>>16,a=M[r+36>>1]|M[r+38>>1]<<16,i=M[r+32>>1]|M[r+34>>1]<<16,b[t+32>>1]=i,b[t+34>>1]=i>>>16,b[t+36>>1]=a,b[t+38>>1]=a>>>16,a=M[r+28>>1]|M[r+30>>1]<<16,i=M[r+24>>1]|M[r+26>>1]<<16,b[t+24>>1]=i,b[t+26>>1]=i>>>16,b[t+28>>1]=a,b[t+30>>1]=a>>>16,a=M[r+20>>1]|M[r+22>>1]<<16,i=M[r+16>>1]|M[r+18>>1]<<16,b[t+16>>1]=i,b[t+18>>1]=i>>>16,b[t+20>>1]=a,b[t+22>>1]=a>>>16,a=M[r+12>>1]|M[r+14>>1]<<16,i=M[r+8>>1]|M[r+10>>1]<<16,b[t+8>>1]=i,b[t+10>>1]=i>>>16,b[t+12>>1]=a,b[t+14>>1]=a>>>16,g[s+177904|0]=0,b[t>>1]=32768|M[t>>1],a=t))),t=M[E+2>>1]-p|0}s=1,b[2+((c<<1)+a|0)>>1]=t,e[o+8>>2]=a}if((0|(c=c+1|0))==6)break}I=a}if((0|f)!=(0|m))continue;break A}break}I=r}for(a=0;;){if((r=e[(m=216192+(T<<4)|0)>>2])-5>>>0<2)break e;if((0|r)<=4){if(r=e[m+8>>2],t=M[m+4>>1],a){if((0|r)!=(0|a))break e;e[m+8>>2]=I}else I=r;if(16&(a=M[I>>1]))break e;for(E=32&a?(z(t,6)>>>0)/5|0:t,s=0,r=a=e[m+12>>2],c=0;;){A:{r:if((0|(f=(o=b[2+((t=c<<1)+a|0)>>1])-(t=b[2+(p=t+I|0)>>1])|0))>(0|(o=(0|z(E,(0|z(e[200944+(c<<2)>>2],(0|f)>0?o+(t<<1)|0:(o<<1)+t|0))/3e3|0))/256|0))){if(!s){if(b[a>>1]<0){r=a,t=t+o|0;break r}r=(0|(r=e[44469]+1|0))<=169?r:0,e[44469]=r,s=M[a+20>>1]|M[a+22>>1]<<16,t=16+(r=177888+(r<<6)|0)|0,f=M[a+16>>1]|M[a+18>>1]<<16,b[t>>1]=f,b[t+2>>1]=f>>>16,b[t+4>>1]=s,b[t+6>>1]=s>>>16,t=M[a+4>>1]|M[a+6>>1]<<16,s=M[a>>1]|M[a+2>>1]<<16,b[r>>1]=s,b[r+2>>1]=s>>>16,b[r+4>>1]=t,b[r+6>>1]=t>>>16,t=M[a+12>>1]|M[a+14>>1]<<16,s=M[a+8>>1]|M[a+10>>1]<<16,b[r+8>>1]=s,b[r+10>>1]=s>>>16,b[r+12>>1]=t,b[r+14>>1]=t>>>16,t=M[a+28>>1]|M[a+30>>1]<<16,s=M[a+24>>1]|M[a+26>>1]<<16,b[r+24>>1]=s,b[r+26>>1]=s>>>16,b[r+28>>1]=t,b[r+30>>1]=t>>>16,t=M[a+36>>1]|M[a+38>>1]<<16,s=M[a+32>>1]|M[a+34>>1]<<16,b[r+32>>1]=s,b[r+34>>1]=s>>>16,b[r+36>>1]=t,b[r+38>>1]=t>>>16,t=M[a+44>>1]|M[a+46>>1]<<16,s=M[a+40>>1]|M[a+42>>1]<<16,b[r+40>>1]=s,b[r+42>>1]=s>>>16,b[r+44>>1]=t,b[r+46>>1]=t>>>16,t=M[a+52>>1]|M[a+54>>1]<<16,s=M[a+48>>1]|M[a+50>>1]<<16,b[r+48>>1]=s,b[r+50>>1]=s>>>16,b[r+52>>1]=t,b[r+54>>1]=t>>>16,t=M[a+60>>1]|M[a+62>>1]<<16,s=M[a+56>>1]|M[a+58>>1]<<16,b[r+56>>1]=s,b[r+58>>1]=s>>>16,b[r+60>>1]=t,b[r+62>>1]=t>>>16,g[r+16|0]=0,b[r>>1]=32768|M[r>>1],t=M[p+2>>1]}t=t+o|0}else{if((0|f)>=(0-o|0))break A;s||(b[a>>1]<0?r=a:(r=(0|(r=e[44469]+1|0))<=169?r:0,e[44469]=r,s=M[a+20>>1]|M[a+22>>1]<<16,t=16+(r=177888+(r<<6)|0)|0,f=M[a+16>>1]|M[a+18>>1]<<16,b[t>>1]=f,b[t+2>>1]=f>>>16,b[t+4>>1]=s,b[t+6>>1]=s>>>16,t=M[a+4>>1]|M[a+6>>1]<<16,s=M[a>>1]|M[a+2>>1]<<16,b[r>>1]=s,b[r+2>>1]=s>>>16,b[r+4>>1]=t,b[r+6>>1]=t>>>16,t=M[a+12>>1]|M[a+14>>1]<<16,s=M[a+8>>1]|M[a+10>>1]<<16,b[r+8>>1]=s,b[r+10>>1]=s>>>16,b[r+12>>1]=t,b[r+14>>1]=t>>>16,t=M[a+28>>1]|M[a+30>>1]<<16,s=M[a+24>>1]|M[a+26>>1]<<16,b[r+24>>1]=s,b[r+26>>1]=s>>>16,b[r+28>>1]=t,b[r+30>>1]=t>>>16,t=M[a+36>>1]|M[a+38>>1]<<16,s=M[a+32>>1]|M[a+34>>1]<<16,b[r+32>>1]=s,b[r+34>>1]=s>>>16,b[r+36>>1]=t,b[r+38>>1]=t>>>16,t=M[a+44>>1]|M[a+46>>1]<<16,s=M[a+40>>1]|M[a+42>>1]<<16,b[r+40>>1]=s,b[r+42>>1]=s>>>16,b[r+44>>1]=t,b[r+46>>1]=t>>>16,t=M[a+52>>1]|M[a+54>>1]<<16,s=M[a+48>>1]|M[a+50>>1]<<16,b[r+48>>1]=s,b[r+50>>1]=s>>>16,b[r+52>>1]=t,b[r+54>>1]=t>>>16,t=M[a+60>>1]|M[a+62>>1]<<16,s=M[a+56>>1]|M[a+58>>1]<<16,b[r+56>>1]=s,b[r+58>>1]=s>>>16,b[r+60>>1]=t,b[r+62>>1]=t>>>16,g[r+16|0]=0,b[r>>1]=32768|M[r>>1],t=M[p+2>>1])),t=t-o|0}s=1,b[2+((c<<1)+r|0)>>1]=t,e[m+12>>2]=r}if((0|(c=c+1|0))==6)break}I=r}if((0|A)==(0|(T=(0|(r=T+1|0))<=169?r:0)))break}}e[36454]=A}}function Ia(A,t){var r,a,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0,MA=0,zA=0;J=r=J-48|0,be(+A),i=0|X(1),s=0|X(0),a=i;e:{A:{r:{if((f=2147483647&i)>>>0<=1074752122){if((1048575&i)==598523)break r;if(f>>>0<=1073928572){if((0|a)>0|(0|a)>=0){c=(A+=-1.5707963267341256)+-6077100506506192e-26,D[t>>3]=c,D[t+8>>3]=A-c-6077100506506192e-26,i=1;break e}c=(A+=1.5707963267341256)+6077100506506192e-26,D[t>>3]=c,D[t+8>>3]=A-c+6077100506506192e-26,i=-1;break e}if((0|a)>0|(0|a)>=0){c=(A+=-3.1415926534682512)+-12154201013012384e-26,D[t>>3]=c,D[t+8>>3]=A-c-12154201013012384e-26,i=2;break e}c=(A+=3.1415926534682512)+12154201013012384e-26,D[t>>3]=c,D[t+8>>3]=A-c+12154201013012384e-26,i=-2;break e}if(f>>>0<=1075594811){if(f>>>0<=1075183036){if((0|f)==1074977148)break r;if((0|a)>0|(0|a)>=0){c=(A+=-4.712388980202377)+-18231301519518578e-26,D[t>>3]=c,D[t+8>>3]=A-c-18231301519518578e-26,i=3;break e}c=(A+=4.712388980202377)+18231301519518578e-26,D[t>>3]=c,D[t+8>>3]=A-c+18231301519518578e-26,i=-3;break e}if((0|f)==1075388923)break r;if((0|a)>0|(0|a)>=0){c=(A+=-6.2831853069365025)+-2430840202602477e-25,D[t>>3]=c,D[t+8>>3]=A-c-2430840202602477e-25,i=4;break e}c=(A+=6.2831853069365025)+2430840202602477e-25,D[t>>3]=c,D[t+8>>3]=A-c+2430840202602477e-25,i=-4;break e}if(f>>>0>1094263290)break A}s=(Qe=(c=A+-1.5707963267341256*(I=.6366197723675814*A+6755399441055744-6755399441055744))-(T=6077100506506192e-26*I))<-.7853981633974483,i=Ne(I)<2147483648?~~I:-2147483648,s?(i=i-1|0,T=6077100506506192e-26*(I+=-1),c=A+-1.5707963267341256*I):Qe>.7853981633974483&&(i=i+1|0,T=6077100506506192e-26*(I+=1),c=A+-1.5707963267341256*I),A=c-T,D[t>>3]=A,be(+A),s=0|X(1),X(0),((o=f>>>20|0)-(s>>>20&2047)|0)<17||(T=c,A=(c-=A=6077100506303966e-26*I)-(T=20222662487959506e-37*I-(T-c-A)),D[t>>3]=A,be(+A),s=0|X(1),X(0),(o-(s>>>20&2047)|0)<50||(T=c,A=(c-=A=20222662487111665e-37*I)-(T=84784276603689e-45*I-(T-c-A)),D[t>>3]=A)),D[t+8>>3]=c-A-T;break e}if(f>>>0>=2146435072)A-=A,D[t>>3]=A,D[t+8>>3]=A,i=0;else{for(H(0,0|s),H(1,1048575&a|1096810496),A=+re(),i=0,s=1;o=(r+16|0)+(i<<3)|0,c=+(0|(i=Ne(A)<2147483648?~~A:-2147483648)),D[o>>3]=c,A=16777216*(A-c),i=1,o=s,s=0,o;);for(D[r+32>>3]=A,i=2;i=(s=i)-1|0,D[(r+16|0)+(s<<3)>>3]==0;);if(We=r+16|0,o=0,J=m=J-560|0,f=z(ve=(0|(f=((i=(f>>>20|0)-1046|0)-3|0)/24|0))>0?f:0,-24)+i|0,((L=e[28105])+(p=(ce=s+1|0)-1|0)|0)>=0)for(i=L+ce|0,s=ve-p|0;D[(m+320|0)+(o<<3)>>3]=(0|s)<0?0:+e[112432+(s<<2)>>2],s=s+1|0,(0|i)!=(0|(o=o+1|0)););for(ee=f-24|0,i=0,o=(0|L)>0?L:0,Z=(0|ce)<=0;;){if(Z)A=0;else for(E=i+p|0,s=0,A=0;A=D[(s<<3)+We>>3]*D[(m+320|0)+(E-s<<3)>>3]+A,(0|ce)!=(0|(s=s+1|0)););if(D[(i<<3)+m>>3]=A,s=(0|i)==(0|o),i=i+1|0,s)break}MA=47-f|0,Ke=48-f|0,zA=f-25|0,i=L;A:{for(;;){if(A=D[(i<<3)+m>>3],s=0,o=i,!(E=(0|i)<=0))for(;Z=(m+480|0)+(s<<2)|0,p=Ne(c=5960464477539063e-23*A)<2147483648?~~c:-2147483648,p=Ne(A=-16777216*(c=+(0|p))+A)<2147483648?~~A:-2147483648,e[Z>>2]=p,A=D[((o=o-1|0)<<3)+m>>3]+c,(0|i)!=(0|(s=s+1|0)););A=Ws(A,ee),A+=-8*AA(.125*A),A-=+(0|(Z=Ne(A)<2147483648?~~A:-2147483648));r:{s:{i:{if(rA=(0|ee)<=0){if(ee)break i;p=e[476+((i<<2)+m|0)>>2]>>23}else he=o=(i<<2)+m|0,o=(p=e[o+476>>2])-((s=p>>Ke)<<Ke)|0,e[he+476>>2]=o,Z=s+Z|0,p=o>>MA;if((0|p)<=0)break r;break s}if(p=2,!(A>=.5)){p=0;break r}}if(s=0,o=0,!E)for(;cA=e[(he=(m+480|0)+(s<<2)|0)>>2],E=16777215,o||(E=16777216,cA)?(e[he>>2]=E-cA,o=1):o=0,(0|i)!=(0|(s=s+1|0)););s:if(!rA){s=8388607;i:switch(0|zA){case 1:s=4194303;break;case 0:break i;default:break s}e[476+(E=(i<<2)+m|0)>>2]=e[E+476>>2]&s}Z=Z+1|0,(0|p)==2&&(A=1-A,p=2,o&&(A-=Ws(1,ee)))}if(A!=0)break;if(o=0,!((0|L)>=(0|(s=i)))){for(;o=e[(m+480|0)+((s=s-1|0)<<2)>>2]|o,(0|s)>(0|L););if(o){for(f=ee;f=f-24|0,!e[(m+480|0)+((i=i-1|0)<<2)>>2];);break A}}for(s=1;o=s,s=s+1|0,!e[(m+480|0)+(L-o<<2)>>2];);for(o=i+o|0;;){if(p=i+ce|0,i=i+1|0,D[(m+320|0)+(p<<3)>>3]=e[112432+(ve+i<<2)>>2],s=0,A=0,(0|ce)>0)for(;A=D[(s<<3)+We>>3]*D[(m+320|0)+(p-s<<3)>>3]+A,(0|ce)!=(0|(s=s+1|0)););if(D[(i<<3)+m>>3]=A,!((0|i)<(0|o)))break}i=o}(A=Ws(A,24-f|0))>=16777216?(ee=(m+480|0)+(i<<2)|0,s=Ne(c=5960464477539063e-23*A)<2147483648?~~c:-2147483648,o=Ne(A=-16777216*+(0|s)+A)<2147483648?~~A:-2147483648,e[ee>>2]=o,i=i+1|0):(s=Ne(A)<2147483648?~~A:-2147483648,f=ee),e[(m+480|0)+(i<<2)>>2]=s}if(A=Ws(1,f),!((0|i)<0)){for(s=i;o=s,D[(s<<3)+m>>3]=A*+e[(m+480|0)+(s<<2)>>2],s=s-1|0,A*=5960464477539063e-23,o;);if(E=0,!((0|i)<0))for(f=(0|L)>0?L:0,o=i;;){for(ee=f>>>0<E>>>0?f:E,L=i-o|0,s=0,A=0;A=D[115200+(s<<3)>>3]*D[(s+o<<3)+m>>3]+A,ce=(0|s)!=(0|ee),s=s+1|0,ce;);if(D[(m+160|0)+(L<<3)>>3]=A,o=o-1|0,s=(0|i)!=(0|E),E=E+1|0,!s)break}}if(A=0,(0|i)>=0)for(s=i;o=s,s=s-1|0,A+=D[(m+160|0)+(o<<3)>>3],o;);if(D[r>>3]=p?-A:A,A=D[m+160>>3]-A,s=1,(0|i)>0)for(;A+=D[(m+160|0)+(s<<3)>>3],o=(0|s)!=(0|i),s=s+1|0,o;);D[r+8>>3]=p?-A:A,J=m+560|0,i=7&Z,A=D[r>>3],(0|a)<0?(D[t>>3]=-A,D[t+8>>3]=-D[r+8>>3],i=0-i|0):(D[t>>3]=A,D[t+8>>3]=D[r+8>>3])}}return J=r+48|0,i}function ZA(){var A=0,t=0,r=0,a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0,MA=0,zA=0,et=0,at=0,dt=0,Ft=0,Zt=0,er=0,pr=0,hr=0,yr=0,Er=0,Fr=0,Wr=0,ia=0,Xr=0,na=0,Ba=0,es=0,ns=0,os=0;e[55925]=0,A=e[56772],e[56772]=A+1,r=e[55961],E=e[55922],L=Oi(39.89822670059037*(t=+(0|A))),m=Oi(22.30530784048753*t),t=+(0|E)/50*(+(0|r)/100)*(Oi(14.765485471872028*t)+(L+m))*10,A=Ne(t)<2147483648?~~t:-2147483648,e[56607]=A+e[56607];e:if(!(e[55923]<=0)){for(;;){for(r=It(e[33209],0,1103515245,0),A=$,A=Rn(r=r+12345|0,A=r>>>0<12345?A+1|0:A),e[33209]=A,A=8191+((A>>>0)%16383|0)|0,e[55929]=A,dt=+(0|A),t=.75*D[28387]+dt,D[28387]=t,Ft=(0|(A=e[55924]))>(0|(Ke=e[55928]))?.5*t:t,hr=(s=.033*+e[56652])>0?1-s:1,r=e[56650]<<2,MA=(0|(cA=e[55921]))==1&&(0|r)>263?263:r,yr=111136+((Zt=e[56651])<<1)|0,Er=111136+((zA=e[56658])<<1)|0,Fr=(0|zA)>0,er=e[55918],Wr=z(er,40),ia=e[55925],Xr=D[27967],pr=e[56607],t=D[27979],ee=D[27969],c=D[27970],et=e[56780],ve=e[56654],I=D[27976],T=D[27972],ce=D[28388],he=D[28389],E=e[55927],a=D[28383],p=e[55926],Qe=e[55956],na=D[27975],m=D[28131],i=D[28130],o=D[28123],Z=D[28121],f=D[28122],rA=D[28120],We=D[28119],Ba=D[28129],es=D[28128],ns=D[28127],os=Ga(0*D[27968]),at=0;;){L=i;A:{r:switch(cA-1|0){case 0:ce=0,i=Z*o,o=f,f=a=i+(We*(ce=(0|A)<=2?D[111312+(A<<3)>>3]:ce)+rA*o);break A;case 1:if(a=0,(0|A)>=(0|E)){he=0;break A}i=D[27965]-D[27966],D[27965]=i,a=.028*(he=i+he);break A;case 2:if(!p){Qe=100,a=0;break A}Qe=100,r=Ne(i=+(0|A)/+(0|p)*100)<2147483648?~~i:-2147483648,a=+b[110928+((0|r)%100<<1)>>1],a=t*((+b[110928+((r+1|0)%100<<1)>>1]-a)*(i-+(0|r))+a);break A;case 3:break r;default:break A}p?(Qe=256,r=Ne(i=+(0|A)/+(0|p)*256)<2147483648?~~i:-2147483648,a=+b[111344+((0|r)%256<<1)>>1],a=t*((+b[111344+((r+1|0)%256<<1)>>1]-a)*(i-+(0|r))+a)):(Qe=256,a=0)}if((0|A)>=(0|p)&&((0|pr)>0?(A=(0|Wr)/(0|pr)|0,I=0,T=0,T=zA>>>0<=87?.001*+b[Er>>1]:T,I=Zt>>>0<=87?.001*+b[yr>>1]*.1:I,Ke=A>>Fr,E=(0|(r=(p=(A-1|0)<=(0|MA))?A-2|0:MA))<=40?40:r,i=+b[111776+((E=p||(0|r)<40?E:MA)<<1)>>1],D[27966]=i,Z=i,i=+(0|E),D[27965]=Z*i*.333,ve=(0|(r=A-E|0))>(0|ve)?ve:r,et=0-(r=(0|et)<0?0-ve|0:ve)|0,We=(i*=.00833)*i,We*=1-(rA=(Z=(i=Pt(Xr*+((0|er)/(0|E)|0)))*os)+Z)-(Z=i*-i),A=(0|(p=A+r|0))!=4):(e[55930]=0,e[55931]=0,e[55932]=0,e[55933]=0,Ke=4,I=0,T=0,p=4,A=0),c=(A|=!ia)?s:c,ee=A?hr:ee,A=0),A=A+1|0,i=a=Ba*m+(ns*a+es*L),m=L,(0|(at=at+1|0))==4)break}if(e[55926]=p,e[55956]=Qe,e[55924]=A,D[28383]=a,e[55927]=E,D[28389]=he,D[28388]=ce,D[27972]=T,D[27976]=I,e[55928]=Ke,e[56654]=ve,e[56780]=et,D[27970]=c,D[27969]=ee,D[28122]=f,D[28120]=rA,D[28119]=We,D[28123]=o,D[28121]=Z,D[28131]=m,D[28130]=a,(0|cA)==5&&(a=6e3*((t=+(0|A)/+(0|p))+t+-1),D[28383]=a),t=a*ee+D[28384]*c,D[28383]=t,D[28384]=t,(0|A)<(0|E)&&(t=I*dt+t,D[28383]=t),c=Ft*D[27974],L=t*D[27973]+c,m=0,e[55916]!=2&&(m=D[27987],f=D[27986],D[27987]=f,t=c+t*T,D[27986]=t,c=D[28059],o=D[28058],D[28059]=o,t=c*D[28057]+(D[28055]*(m*D[27985]+(D[27983]*t+f*D[27984]))+o*D[28056]),D[28058]=t,m=D[28049],c=D[28051],f=D[28048],o=D[28047],s=D[28050],D[28051]=s,t=m*c+(o*t+f*s),D[28050]=t,m=D[28043],c=D[28041],f=D[28040],o=D[28039],s=D[28042],D[28043]=s,t=c*m+(o*t+f*s),D[28042]=t,m=D[28035],c=D[28033],f=D[28032],o=D[28031],s=D[28034],D[28035]=s,t=c*m+(o*t+f*s),D[28034]=t,m=D[28027],c=D[28025],f=D[28024],o=D[28023],s=D[28026],D[28027]=s,t=c*m+(o*t+f*s),D[28026]=t,m=D[28019],c=D[28017],f=D[28016],o=D[28015],s=D[28018],D[28019]=s,t=c*m+(o*t+f*s),D[28018]=t,m=D[28011],c=D[28009],f=D[28008],o=D[28007],s=D[28010],D[28011]=s,t=c*m+(o*t+f*s),D[28010]=t,m=D[28003],c=D[28001],f=D[28e3],o=D[27999],s=D[28002],D[28003]=s,t=c*m+(o*t+f*s),D[28002]=t,m=D[27995],c=D[27993],f=D[27992],o=D[27991],s=D[27994],D[27995]=s,m=c*m+(o*t+f*s),D[27994]=m),t=D[28385],D[28385]=L,c=D[28075],f=D[28074],D[28075]=f,o=D[28067],s=D[28066],D[28067]=s,c=c*D[28073]+(D[28071]*L+f*D[28072]),D[28074]=c,f=o*D[28065]+(D[28063]*L+s*D[28064]),D[28066]=f,o=D[28081],s=D[28083],i=D[28079],a=D[28080],I=D[28082],D[28083]=I,L=o*s+(i*(t=na*Ft+L-t)+a*I),D[28082]=L,o=D[28091],s=D[28089],i=D[28087],a=D[28088],I=D[28090],D[28091]=I,o=s*o+(i*t+a*I),D[28090]=o,s=D[28099],i=D[28097],a=D[28095],I=D[28096],T=D[28098],D[28099]=T,s=i*s+(a*t+I*T),D[28098]=s,i=D[28107],a=D[28105],I=D[28103],T=D[28104],ee=D[28106],D[28107]=ee,i=a*i+(I*t+T*ee),D[28106]=i,a=D[28115],I=D[28113],T=D[28111],ee=D[28112],ce=D[28114],D[28115]=ce,a=I*a+(T*t+ee*ce),D[28114]=a,I=D[28139],T=D[28137],ee=D[28136],ce=D[28135],he=D[27971],Z=D[28138],D[28139]=Z,t=T*I+(ce*(t*he-(a-(i-(s-(o-(L-(m+c+f)))))))+ee*Z),D[28138]=t,t=D[27977]*(t*+e[50779]),a=+(0|(A=Ne(t)<2147483648?~~t:-2147483648)),(0|(A=e[50776]))<e[50773]&&(p=A+1|0,r=e[50772],(E=e[50774])?(e[50776]=p,A=z(E,g[A+r|0])):(E=l[A+r|0],r=g[r+p|0],e[50776]=A+2,A=E|r<<8),a+=+((0|z(e[50775],(0|z(A,e[50780]))/1024|0))/40|0)),(0|(A=e[55962]))<=63&&(e[55962]=A+1,a=a*+(0|A)*.015625),(0|(A=e[55963]))<=0||(A=A-1|0,e[55963]=A,a=a*+(0|A)*.015625,A||(e[55962]=0)),r=(A=e[51293])+1|0,e[51293]=r,p=z(b[205184+(A<<1)>>1],e[50755])>>8,A=Ne(a)<2147483648?~~a:-2147483648,(0|r)>=5500&&(e[51293]=0),r=e[51290],e[51290]=r+1,A=(0|(A=(0|(A=A+p|0))<=-32768?-32768:A))>=32767?32767:A,g[0|r]=A,r=e[51290],e[51290]=r+1,g[0|r]=A>>>8,p=(r=e[51292])+1|0,e[51292]=p,b[205184+(r<<1)>>1]=A,(0|p)>=5500&&(e[51292]=0),p=1,e[56606]=e[56606]+1,ue[54046]<e[51290]+2>>>0)break e;if(A=e[55925]+1|0,e[55925]=A,!((0|A)<e[55923]))break}p=0}return p}function $A(A,t,r,a,s,i,o){var c,f,m,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0,MA=0,zA=0;J=c=J-80|0,e[c+76>>2]=t,m=c+55|0,f=c+56|0;e:{A:{r:{s:{i:for(;;){if(E=t,(2147483647^ce)<(0|p))break s;ce=p+ce|0;c:{o:{u:{if(I=l[0|(p=E)])for(;;){l:{n:if(t=255&I){if((0|t)!=37)break l;for(I=p;;){if(l[I+1|0]!=37){t=I;break n}if(p=p+1|0,L=l[I+2|0],I=t=I+2|0,(0|L)!=37)break}}else t=p;if((0|(p=p-E|0))>(0|(cA=2147483647^ce)))break s;if(A&&Zr(A,E,p),p)continue i;e[c+76>>2]=t,p=t+1|0,he=-1,l[t+2|0]!=36|g[t+1|0]-48>>>0>=10||(he=g[t+1|0]-48|0,We=1,p=t+3|0),e[c+76>>2]=p,Z=0;n:if((t=(I=g[0|p])-32|0)>>>0>31)ee=p;else if(ee=p,75913&(t=1<<t))for(;;){if(ee=p+1|0,e[c+76>>2]=ee,Z|=t,(t=(I=g[p+1|0])-32|0)>>>0>=32)break n;if(p=ee,!(75913&(t=1<<t)))break}n:if((0|I)!=42){if((0|(ve=m0(c+76|0)))<0)break s;I=e[c+76>>2]}else{if(l[ee+2|0]!=36|g[ee+1|0]-48>>>0>=10){if(We)break u;if(I=ee+1|0,!A){e[c+76>>2]=I,We=0,ve=0;break n}t=e[r>>2],e[r>>2]=t+4,We=0,t=e[t>>2]}else e[((g[ee+1|0]<<2)+s|0)-192>>2]=10,I=ee+3|0,We=1,t=e[((g[ee+1|0]<<3)+a|0)-384>>2];if(e[c+76>>2]=I,ve=t,(0|t)>=0)break n;ve=0-ve|0,Z|=8192}if(p=0,T=-1,l[0|I]==46)if(l[I+1|0]!=42)e[c+76>>2]=I+1,T=m0(c+76|0),t=e[c+76>>2],rA=1;else{if(l[I+3|0]!=36|g[I+2|0]-48>>>0>=10){if(We)break u;t=I+2|0,T=0,A&&(I=e[r>>2],e[r>>2]=I+4,T=e[I>>2])}else e[((g[I+2|0]<<2)+s|0)-192>>2]=10,t=I+4|0,T=e[((g[I+2|0]<<3)+a|0)-384>>2];e[c+76>>2]=t,rA=~T>>>31|0}else t=I,rA=0;for(;;){if(Qe=p,ee=28,L=t,(p=g[0|t])-123>>>0<4294967238)break r;if(t=L+1|0,!((p=l[123983+(p+z(Qe,58)|0)|0])-1>>>0<8))break}e[c+76>>2]=t;n:{p:{if((0|p)!=27){if(!p)break r;if((0|he)>=0){e[(he<<2)+s>>2]=p,p=e[4+(I=(he<<3)+a|0)>>2],e[c+64>>2]=e[I>>2],e[c+68>>2]=p;break p}if(!A)break c;Vt(c- -64|0,p,r,o);break n}if((0|he)>=0)break r}if(p=0,!A)continue i}I=-65537&Z,Z=8192&Z?I:Z,he=0,Ke=84065,ee=f;n:{p:{C:{m:{b:{h:{E:{w:{y:{L:{W:{V:{M:{B:{D:{switch(p=g[0|L],(p=Qe&&(15&p)==3?-33&p:p)-88|0){case 11:break n;case 9:case 13:case 14:case 15:break p;case 27:break E;case 12:case 17:break L;case 23:break W;case 0:case 32:break V;case 24:break M;case 22:break B;case 29:break D;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 10:case 16:case 18:case 19:case 20:case 21:case 25:case 26:case 28:case 30:case 31:break o}switch(p-65|0){case 0:case 4:case 5:case 6:break p;case 2:break b;case 1:case 3:break o}if((0|p)==83)break h;break o}I=e[c+64>>2],L=e[c+68>>2],Ke=84065;break y}p=0;B:switch(255&Qe){case 0:case 1:case 6:e[e[c+64>>2]>>2]=ce;continue i;case 2:E=e[c+64>>2],e[E>>2]=ce,e[E+4>>2]=ce>>31;continue i;case 3:b[e[c+64>>2]>>1]=ce;continue i;case 4:g[e[c+64>>2]]=ce;continue i;case 7:break B;default:continue i}E=e[c+64>>2],e[E>>2]=ce,e[E+4>>2]=ce>>31;continue i}T=T>>>0<=8?8:T,Z|=8,p=120}if(E=f,MA=32&p,(I=e[c+64>>2])|(L=e[c+68>>2]))for(;g[0|(E=E-1|0)]=MA|l[124512+(15&I)|0],zA=!L&I>>>0>15|!!(0|L),Qe=L,L=L>>>4|0,I=(15&Qe)<<28|I>>>4,zA;);if(!(e[c+64>>2]|e[c+68>>2])|!(8&Z))break w;Ke=84065+(p>>>4|0)|0,he=2;break w}if(p=f,L=E=e[c+68>>2],E|(I=e[c+64>>2]))for(;g[0|(p=p-1|0)]=7&I|48,Qe=!L&I>>>0>7|!!(0|L),L=(E=L)>>>3|0,I=(7&E)<<29|I>>>3,Qe;);if(E=p,!(8&Z))break w;T=(0|(p=f-E|0))<(0|T)?T:p+1|0;break w}I=e[c+64>>2],L=p=e[c+68>>2],(0|p)<0?(L=E=0-(L+!!(0|I)|0)|0,I=0-I|0,e[c+64>>2]=I,e[c+68>>2]=E,he=1,Ke=84065):2048&Z?(he=1,Ke=84066):Ke=(he=1&Z)?84067:84065}E=fs(I,L,f)}if((0|T)<0&&rA)break s;if(Z=rA?-65537&Z:Z,!(T|!!((p=e[c+64>>2])|(I=e[c+68>>2])))){E=f,T=0;break o}T=(0|(p=!(p|I)+(f-E|0)|0))<(0|T)?T:p;break o}if(ee=(p=(p=ri(E=(p=e[c+64>>2])||84639,0,L=T>>>0>=2147483647?2147483647:T))?p-E|0:L)+E|0,(0|T)>=0){Z=I,T=p;break o}if(Z=I,T=p,l[0|ee])break s;break o}if(T){I=e[c+64>>2];break m}p=0,Aa(A,32,ve,0,Z);break C}e[c+12>>2]=0,e[c+8>>2]=e[c+64>>2],I=c+8|0,e[c+64>>2]=I,T=-1}p=0;m:{for(;;){if(!(E=e[I>>2]))break m;if(!((E=(0|(L=n0(c+4|0,E)))<0)|L>>>0>T-p>>>0)){if(I=I+4|0,T>>>0>(p=p+L|0)>>>0)continue;break m}break}if(E)break A}if(ee=61,(0|p)<0)break r;if(Aa(A,32,ve,p,Z),p)for(ee=0,I=e[c+64>>2];;){if(!(E=e[I>>2])||(ee=(E=n0(c+4|0,E))+ee|0)>>>0>p>>>0)break C;if(Zr(A,c+4|0,E),I=I+4|0,!(p>>>0>ee>>>0))break}else p=0}Aa(A,32,ve,p,8192^Z),p=(0|p)<(0|ve)?ve:p;continue i}if((0|T)<0&&rA)break s;if(ee=61,(0|(p=0|ct[0|i](A,D[c+64>>3],ve,T,Z,p)))>=0)continue i;break r}g[c+55|0]=e[c+64>>2],T=1,E=m,Z=I;break o}I=l[p+1|0],p=p+1|0}if(A)break e;if(!We)break c;for(p=1;;){if(A=e[(p<<2)+s>>2]){if(Vt((p<<3)+a|0,A,r,o),ce=1,(0|(p=p+1|0))!=10)continue;break e}break}if(ce=1,p>>>0>=10)break e;for(;;){if(e[(p<<2)+s>>2])break u;if((0|(p=p+1|0))==10)break}break e}ee=28;break r}if((0|(I=(0|T)>(0|(L=ee-E|0))?T:L))>(2147483647^he))break s;if(ee=61,(0|cA)<(0|(p=(0|(T=I+he|0))<(0|ve)?ve:T)))break r;Aa(A,32,p,T,Z),Zr(A,Ke,he),Aa(A,48,p,T,65536^Z),Aa(A,48,I,L,0),Zr(A,E,L),Aa(A,32,p,T,8192^Z);continue}break}ce=0;break e}ee=61}e[56798]=ee}ce=-1}return J=c+80|0,ce}function PA(A,t,r,a,s,i,o,c,f){var m,p,I,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0,MA=0,zA=0,et=0,at=0,dt=0,Ft=0,Zt=0,er=0,pr=0,hr=0,yr=0,Er=0,Fr=0,Wr=0,ia=0,Xr=0,na=0,Ba=0;J=m=J-96|0,Ke=65535&f,ee=-2147483648&(s^f),et=he=65535&s;e:{if(!((p=f>>>16&32767)-32767>>>0>4294934529&(I=s>>>16&32767)-32767>>>0>=4294934530)){if(E=a,!(!a&(0|(ve=Qe=2147483647&s))==2147418112?!(t|r):ve>>>0<2147418112)){L=a,ee=32768|s;break e}if(!(!(s=c)&(0|(ce=Qe=2147483647&f))==2147418112?!(i|o):ce>>>0<2147418112)){L=c,ee=32768|f,t=i,r=o;break e}if(!(t|E|2147418112^ve|r)){if(!(s|i|o|ce)){ee=2147450880,t=0,r=0;break e}ee|=2147418112,t=0,r=0;break e}if(!(s|i|2147418112^ce|o)){if(s=t|E,a=r|ve,t=0,r=0,!(a|s)){ee=2147450880;break e}ee|=2147418112;break e}if(!(t|E|r|ve)){t=0,r=0;break e}if(!(s|i|o|ce)){t=0,r=0;break e}(0|ve)==65535|ve>>>0<65535&&(Qe=(E=!(a|he))<<6,s=Ye(f=E?t:a)+32|0,ea(m+80|0,t,r,a,he,(f=Qe+((0|(f=Ye(E?r:he)))==32?s:f)|0)-15|0),We=16-f|0,a=e[m+88>>2],et=e[m+92>>2],r=e[m+84>>2],t=e[m+80>>2]),ce>>>0>65535||(he=(f=!(c|Ke))<<6,E=Ye(s=f?i:c)+32|0,ea(m- -64|0,i,o,c,Ke,(s=he+((0|(s=Ye(f?o:Ke)))==32?E:s)|0)-15|0),We=16+(We-s|0)|0,c=e[m+72>>2],Ke=e[m+76>>2],i=e[m+64>>2],o=e[m+68>>2])}if(s=i,i=o<<15|i>>>17,Fr=r,Qe=It(rA=-32768&(f=s<<15),s=0,r,0),zA=s=$,Wr=i,ve=t,t=It(i,0,t,0),i=$+s|0,r=t>>>0>(f=t+Qe|0)>>>0?i+1|0:i,E=0,t=It(ve,T,rA,T),s=(i=f)+$|0,he=s=t>>>0>(ce=E+t|0)>>>0?s+1|0:s,ia=(0|i)==(0|s)&E>>>0>ce>>>0|s>>>0<i>>>0,Xr=a,cA=It(rA,T,a,0),na=$,t=It(Fr,T,Wr,T),E=$+na|0,E=t>>>0>(MA=t+cA|0)>>>0?E+1|0:E,t=Ke<<15|c>>>17,a=It(at=c<<15|o>>>17,0,ve,T),i=$+E|0,Ft=i=a>>>0>(dt=a+MA|0)>>>0?i+1|0:i,i=(a=(0|r)==(0|zA)&f>>>0<Qe>>>0|r>>>0<zA>>>0)+i|0,Ke=i=r>>>0>(Zt=r+dt|0)>>>0?i+1|0:i,o=Zt,r=i,et=It(rA,T,er=65536|et,Z),Ba=$,a=It(Xr,L,Wr,T),s=$+Ba|0,f=s=a>>>0>(pr=a+et|0)>>>0?s+1|0:s,t=It(hr=-2147483648|t,0,ve,T),i=$+s|0,i=t>>>0>(yr=t+pr|0)>>>0?i+1|0:i,t=It(at,L,Fr,T),Er=i,i=i+$|0,Qe=t>>>0>(zA=t+yr|0)>>>0?i+1|0:i,s=r+zA|0,i=rA=(t=0)>>>0>(ve=t+o|0)>>>0?s+1|0:s,r=(t=ve+ia|0)>>>0<ve>>>0?i+1|0:i,We=((I+p|0)+We|0)-16383|0,a=It(hr,L,Fr,T),c=$,s=It(er,L,Wr,T),i=$+c|0,Z=(0|c)==(0|(i=s>>>0>(o=s+a|0)>>>0?i+1|0:i))&a>>>0>o>>>0|i>>>0<c>>>0,c=i,s=It(at,L,Xr,L),i=$+i|0,s=i=(a=s+o|0)>>>0<s>>>0?i+1|0:i,o=(0|i)==(0|c)&a>>>0<o>>>0|i>>>0<c>>>0,i=0,i=(c=o)>>>0>(o=o+Z|0)>>>0?1:i,c=o,o=It(hr,L,er,L),i=$+i|0,ia=c=c+o|0,o=o>>>0>c>>>0?i+1|0:i,c=a,T=s,s=(0|E)==(0|na)&cA>>>0>MA>>>0|E>>>0<na>>>0,i=0,s=((Z=E=(0|E)==(0|Ft)&MA>>>0>dt>>>0|E>>>0>Ft>>>0)>>>0>(E=s+E|0)>>>0?1:i)+T|0,i=o,Z=s=(a=a+E|0)>>>0<E>>>0?s+1|0:s,cA=a,s=a=(0|s)==(0|T)&a>>>0<c>>>0|s>>>0<T>>>0,E=a=a+ia|0,o=i=s>>>0>a>>>0?i+1|0:i,s=It(at,L,er,L),T=$,a=It(hr,L,Xr,L),i=$+T|0,a=i=a>>>0>(c=a+s|0)>>>0?i+1|0:i,s=(i=(0|T)==(0|i)&s>>>0>c>>>0|i>>>0<T>>>0)+o|0,o=s=a>>>0>(MA=a+E|0)>>>0?s+1|0:s,i=c+Z|0,s=i=(a=(s=0)+cA|0)>>>0<s>>>0?i+1|0:i,c=(0|Z)==(0|i)&a>>>0<cA>>>0|i>>>0<Z>>>0,i=o,i=(o=c+(T=MA)|0)>>>0<c>>>0?i+1|0:i,cA=o,c=a,E=s,s=(a=(a=(a=(0|f)==(0|Ba)&et>>>0>pr>>>0|f>>>0<Ba>>>0)+(f=(0|f)==(0|Er)&pr>>>0>yr>>>0|f>>>0>Er>>>0)|0)+(s=(0|Qe)==(0|Er)&zA>>>0<yr>>>0|Qe>>>0<Er>>>0)|0)+E|0,i=o=i,E=o=(c=(0|(s=(a=f=(Z=Qe)+c|0)>>>0<Z>>>0?s+1|0:s))==(0|E)&c>>>0>a>>>0|s>>>0<E>>>0)+cA|0,o=i=c>>>0>o>>>0?i+1|0:i,c=a,i=0,f=s,s=s+((T=Z=(0|Ke)==(0|rA)&ve>>>0<Zt>>>0|Ke>>>0>rA>>>0)>>>0>(Z=Z+((0|Ke)==(0|Ft)&dt>>>0>Zt>>>0|Ke>>>0<Ft>>>0)|0)>>>0?1:i)|0,i=o,f=i=(o=c=(0|(s=(a=a+Z|0)>>>0<Z>>>0?s+1|0:s))==(0|f)&a>>>0<c>>>0|s>>>0<f>>>0)>>>0>(c=c+E|0)>>>0?i+1|0:i,65536&i?We=We+1|0:(E=he>>>31|0,i=f<<1|c>>>31,c=c<<1|s>>>31,f=i,i=s<<1|a>>>31,a=a<<1|r>>>31,s=i,i=he<<1|ce>>>31,ce<<=1,he=i,i=r<<1|t>>>31,t=t<<1|E,r=i|(o=0)),(0|We)>=32767)ee|=2147418112,t=0,r=0;else{A:{if((0|We)<=0){if((o=1-We|0)>>>0<=127){ea(m+48|0,ce,he,t,r,i=We+127|0),ea(m+32|0,a,s,c,f,i),Ya(m+16|0,ce,he,t,r,o),Ya(m,a,s,c,f,o),ce=e[m+32>>2]|e[m+16>>2]|!!(e[m+48>>2]|e[m+56>>2]|e[m+52>>2]|e[m+60>>2]),he=e[m+36>>2]|e[m+20>>2],t=e[m+40>>2]|e[m+24>>2],r=e[m+44>>2]|e[m+28>>2],a=e[m>>2],s=e[m+4>>2],o=e[m+8>>2],i=e[m+12>>2];break A}t=0,r=0;break e}o=c,i=65535&f|We<<16}L|=o,ee|=i,(!t&(0|r)==-2147483648?!(ce|he):(0|r)>0|(0|r)>=0)?t|ce|-2147483648^r|he?(t=a,r=s):(E=ee,ee=(a=(0|(i=s))==(0|(r=(r=t=1&a)>>>0>(t=t+a|0)>>>0?i+1|0:i))&t>>>0<a>>>0|r>>>0<i>>>0)>>>0>(L=a+L|0)>>>0?E+1|0:E):(a=(0|s)==(0|(r=(t=a+1|0)?s:s+1|0))&t>>>0<a>>>0|r>>>0<s>>>0,s=ee,ee=(L=a+L|0)>>>0<a>>>0?s+1|0:s)}}e[A>>2]=t,e[A+4>>2]=r,e[A+8>>2]=L,e[A+12>>2]=ee,J=m+96|0}function qt(A,t,r,a,s,i,o){var c,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0;J=c=J-480|0,e[c+476>>2]=0,e[c+456>>2]=0,e[c+460>>2]=0,e[c+448>>2]=0,e[c+452>>2]=0,e[c+440>>2]=0,e[c+444>>2]=0,e[c+432>>2]=0,e[c+436>>2]=0,f=0;e:if(e[A+684>>2]){for(L=o?e[o>>2]:L;p=l[t+f|0],g[(c+112|0)+f|0]=p,m=f+1|0,p&&(p=f>>>0<158,f=m,p););if(g[m+(c+112|0)|0]=0,!((ce=268435456&i)|!(8&e[47197]))){m=0;A:if(223&(p=l[0|t]))for(f=0;;){if(g[(c+272|0)+f|0]=p,!(223&(p=l[(m=f+1|0)+t|0])))break A;if(I=f>>>0<118,f=m,!I)break}g[(f=c+272|0)+m|0]=0,e[c+48>>2]=f,fr(e[47195],(0|i)>=0?87019:86877,c+48|0)}e[c+464>>2]=t,e[A+8208>>2]=0,e[A+8212>>2]=0,s&&(g[0|s]=0);A:{r:if(223&(f=l[0|t]))for(he=536870912&i,ve=4096&i,Qe=c+105|0,m=t,p=0;;){I=Gt(c+476|0,m),ee=!!(0|rr(e[c+476>>2]))+ee|0,T=l[7668+(f=(E=255&f)+A|0)|0];s:if(!((Z=e[c+476>>2])-48>>>0<10|Z-2406>>>0<10)|(ee?l[A+170|0]:0)){i:if((p=e[c+476>>2]-e[A+600>>2]|0)>>>0>127||!(p=e[6192+((p<<2)+A|0)>>2])){if(T){for(Z=5168+((E<<2)+A|0)|0,T=T+(f=l[f+7924|0])|0,We=E|l[m+1|0]<<8,p=0;e[7184+(m=(f<<2)+A|0)>>2]==(0|We)&&(e[c+472>>2]=e[c+464>>2],Mt(A,c+472|0,t,2,e[m+6704>>2],c+432|0,i,L),(0|(m=e[c+432>>2]))>0&&(m=m+35|0,e[c+432>>2]=m),p=1,Mt(A,c+464|0,t,1,e[Z>>2],c+448|0,i,L),e[c+448>>2]>(0|m)||(m=e[c+444>>2],e[c+456>>2]=e[c+440>>2],e[c+460>>2]=m,m=e[c+436>>2],e[c+448>>2]=e[c+432>>2],e[c+452>>2]=m,e[c+464>>2]=e[c+472>>2])),T>>>0>(f=f+1|0)>>>0;);if(p)break i}c:{o:{if(!(f=e[5168+((E<<2)+A|0)>>2])){if(Mt(A,c+464|0,t,0,e[A+5168>>2],c+448|0,i,L),e[c+448>>2])break c;if(16&l[188808])break o;if(p=Gt(c+468|0,E=(m=e[c+464>>2])-1|0),f=e[c+468>>2],!(e[A+600>>2]<=0|(0|f)>577)){if(vi(f)){e[c+32>>2]=21,DA(r,87049,c+32|0);break A}f=e[c+468>>2]}if((0|f)==57384&&((0|(T=e[A+92>>2]))<=e[47352]||(e[47352]=T)),li(f)&&((0|(f=e[A+72>>2]))<=e[47352]||(e[47352]=f)),!((T=(f=e[c+468>>2])-192|0)>>>0>413)&&(T=l[T+94240|0])&&(p=p-1|0,!(l[m-2|0]==32&l[p+m|0]==32))){for(e[c+472>>2]=E,g[0|E]=T;I=l[(f=m)+p|0],g[0|f]=I,m=f+1|0,(0|I)!=32;);if((0|p)>0&&jA(f,32,p),e[A+24>>2]&&!((0|Pn(94222,e[c+468>>2]))<=0)){e[c+464>>2]=E,p=0;break s}p=0,g[0|r]=0,e[c+464>>2]=t,e[A+8208>>2]=0,e[A+8212>>2]=0;break s}if(!(f=Hs(f))||(0|(m=e[f+4>>2]))==e[A+600>>2])break o;if((0|m)==e[A+188>>2]){e[c+4>>2]=Mn(c- -64|0,e[A+192>>2]),e[c>>2]=21,DA(r,87218,c);break A}if(!(4&l[f+16|0]))break o;e[c+20>>2]=Mn(c- -64|0,e[f+12>>2]),e[c+16>>2]=21,DA(r,87218,c+16|0);break A}if(Mt(A,c+464|0,t,1,f,c+448|0,i,L),e[c+448>>2])break c}o:if(!((f=e[c+476>>2])-768>>>0<112)){if(rr(f)){if(g[(I+e[c+464>>2]|0)-1|0]<33&(0|ee)<=1)break o;if(g[0|r]=0,!o)break r;e[o>>2]=4096|e[o>>2];break r}jt(A,e[c+476>>2],-1,c+272|0,0),l[c+272|0]&&(e[c+448>>2]=1,e[c+452>>2]=c+272)}e[c+464>>2]=(I+e[c+464>>2]|0)-1;break i}e[A+288>>2]=0}else Mt(A,c+464|0,t,I,p,c+448|0,i,L);if(m=(f=e[c+452>>2])||86135,e[c+452>>2]=m,p=0,!(e[c+448>>2]<=0)){if(f=1|e[c+456>>2],(0|i)<0)break e;if(!(l[0|m]!=21|ve)){qA(r,m);break A}if(!(!(8&e[47197])|ce))i:if(f=e[47195],(0|(I=e[f+76>>2]))>=0&(!I|e[56823]!=(-1073741825&I)))E=e[(I=f+76|0)>>2],e[I>>2]=E||1073741823,e[f+80>>2]==10||(0|(E=e[f+20>>2]))==e[f+16>>2]?f0(f):(e[f+20>>2]=E+1,g[0|E]=10),e[I>>2]=0;else{if(e[f+80>>2]!=10&&(0|(I=e[f+20>>2]))!=e[f+16>>2]){e[f+20>>2]=I+1,g[0|I]=10;break i}f0(f)}if(I=-32769&(f=e[c+456>>2]),e[c+456>>2]=I,!(!s|!I|(1024&f?he:0))){A=e[c+464>>2],qA(s,m),f=I|(Ke=(r=A)-wt(t,A=c+112|0,HA(A))|0,(1151&f)==1024?Ke:0);break e}(f=e[c+460>>2])&&(g[0|f]=69),dn(A,r,a,m)}}else g[c+104|0]=95,wt(Qe,m,I),f=1,g[105+(c+I|0)|0]=0,RA(A,c+104|0,c- -64|0),p-1>>>0<=4294967293&&(f=HA(f=c- -64|0)+f|0,g[0|f]=11,g[f+1|0]=0,f=0),dn(A,r,a,c- -64|0),e[c+464>>2]=m+I,p=f;if(m=e[c+464>>2],!(223&(f=l[0|m])))break}wt(t,A=c+112|0,HA(A))}f=0}return J=c+480|0,f}function cs(A,t){var r,a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0;J=r=J-2976|0,e[t>>2]=1,a=e[A+20>>2],e[(c=r+2960|0)>>2]=e[A+16>>2],e[c+4>>2]=a,a=e[A+12>>2],e[(c=r+2952|0)>>2]=e[A+8>>2],e[c+4>>2]=a,a=e[A+4>>2],e[r+2944>>2]=e[A>>2],e[r+2948>>2]=a,e[50303]||KA();e:if(A=e[r+2948>>2],l[0|A]&&A||((A=e[r+2944>>2])||(A=(A=e[r+2952>>2])||85055,e[r+2944>>2]=A),ar(a=r+80|0,A,60),Ni(a,0),!(i=pa(201216,a))||(e[r+2948>>2]=e[i+4>>2]+1,l[r+2958|0]|l[r+2956|0]|l[r+2957|0]))){ee=r+1536|0,J=L=J-336|0;A:if(!(!(A=e[4+(Z=r+2944|0)>>2])|!l[0|A])){if((0|(ce=HA(A)))>=0){for(a=ce>>>0>=79?79:ce,p=1;A=Ht(g[e[Z+4>>2]+s|0]),g[(L+256|0)+s|0]=A,p=((255&A)==45)+p|0,A=(0|a)!=(0|s),s=s+1|0,A;);if((0|p)!=1)break A}p=1}if((0|(I=e[50303]))<=0)e[ee>>2]=0,A=0;else{for(c=(0|p)>=0;;){m=e[201216+(Qe<<2)>>2];A:if(xa(e[m+8>>2],88032,3)){if((A=e[Z+4>>2])&&xa(A,91687,3)){if(c){A=100;r:if(p){if(a=0,s=e[m+4>>2],!(he=l[0|s])){if(!xa(L+256|0,90013,9))break r;break A}for(;;){for(We=1,f=s+1|0,ve=1,Ke=0,s=0;(0|s)<(0|ce)&&(0|(A=g[(L+256|0)+s|0]))!=45||(A=0),Ke=((E=(0|(T=l[s+f|0]))==45)&!!(0|(ve=(E?0:T)<<24>>24==(0|A)?ve:0)))+Ke|0,s=s+1|0,We=E+We|0,T;);if(s=s+f|0,(E=ve+Ke|0)&&(a=(0|(A=z((f=(0|(A=p-E|0))<=0?5:5-A|0)-((0|(A=We-E|0))>0?A:0)|0,100)-(he<<24>>24<<1)|0))>(0|a)?A:a),!(he=l[0|s]))break}if(!(A=a))break A}(a=e[Z>>2])&&(A=vr(a,e[m>>2])?vr(a,e[m+8>>2])?A:A+400|0:A+500|0),((s=l[Z+12|0])-1&255)>>>0>1||((a=l[m+12|0])-1&255)>>>0>1||(A=(0|a)!=(0|s)?A-50|0:A+50|0),s=l[Z+13|0],A=l[m+12|0]!=2|s>>>0>12?A:l[m+13|0]>12?A+5|0:A,(a=l[m+13|0])&&((a=((s?z(s,100):3e3)>>>0)/(a>>>0)|0)>>>0<=99&&(a=1e4/(a>>>0)|0),A=(f=A)+((A=5-(((a-100&65535)>>>0)/10|0)|0)>>31&A)|0,A=s?A+10|0:A),A=(0|A)<=1?1:A}else{if(xa(e[m+8>>2],L+256|0,ce))break A;A=100}e[ee+(o<<2)>>2]=m,e[m+16>>2]=A}else e[ee+(o<<2)>>2]=m;o=o+1|0}if((0|I)==(0|(Qe=Qe+1|0)))break}e[ee+(o<<2)>>2]=0,A=0,o&&(Ai(ee,o,8),A=o)}if(J=L+336|0,p=A,A||(e[t>>2]=0,A=pa(201216,85055),e[r+1536>>2]=A,p=!!(0|A)),t=l[r+2957|0],c=2,(0|(A=l[r+2956|0]))!=2&&(c=2,(t-1&255)>>>0<12||(cA=(0|A)!=1,c=(0|A)==1)),I=(o=e[132136+(c<<2)>>2])+(T=t>>>0<60)|0,A=0,(0|p)>0)for(a=0;;){i=e[(r+1536|0)+(rA<<2)>>2];A:{r:{s:{if(cA){if(T||(t=0,a))break r}else{if(t=l[i+12|0],a|T)break s;t=(0|t)!=(0|c)}if(s=0,t|l[i+13|0]<60)break A;break r}if((0|t)!=(0|c)){s=a;break A}}e[(r+80|0)+(a<<2)>>2]=i,s=a+1|0}A:if(l[i+15|0]){if(E=0,t=A,a=s,!((0|A)>11))for(;;){if((s=l[0|I])||(I=o,s=l[0|o]),A=e[i+12>>2],f=z(t,24)+202624|0,e[f+8>>2]=e[i+8>>2],e[f+12>>2]=A,A=e[i+4>>2],e[f>>2]=e[i>>2],e[f+4>>2]=A,A=e[i+20>>2],e[f+16>>2]=e[i+16>>2],e[f+20>>2]=A,g[f+14|0]=s,e[(r+80|0)+(a<<2)>>2]=f,I=I+1|0,a=a+1|0,A=t+1|0,(E=E+1|0)>>>0>=l[i+15|0])break A;if(s=(0|t)<11,t=A,!s)break}}else a=s;if((0|(rA=rA+1|0))==(0|p))break}else{if(!i)break e;a=0}A:if(!(!(s=l[0|I])|(0|A)>=12))for(;;){if(t=e[i+12>>2],o=z(A,24)+202624|0,e[o+8>>2]=e[i+8>>2],e[o+12>>2]=t,t=e[i+4>>2],e[o>>2]=e[i>>2],e[o+4>>2]=t,t=e[i+20>>2],e[o+16>>2]=e[i+16>>2],e[o+20>>2]=t,g[o+14|0]=s,e[(r+80|0)+(a<<2)>>2]=o,a=a+1|0,!(s=l[0|(I=I+1|0)]))break A;if(t=(0|A)<11,A=A+1|0,!t)break}a?(A=e[(r+80|0)+(l[r+2958|0]%(0|a)<<2)>>2],(t=l[A+14|0])?(g[202976]=0,e[r+48>>2]=47,DA(r+2971|0,91351,r+48|0),g[r+2971|0]=0,t>>>0<=9?(e[r+20>>2]=t,e[r+16>>2]=r+2971,DA(202976,91378,r+16|0)):(e[r+36>>2]=t-10,e[r+32>>2]=r+2971,DA(202976,91503,r+32|0)),A=e[A+8>>2],e[r+4>>2]=202976,e[r>>2]=A,A=202912,DA(202912,87760,r)):A=e[A+8>>2]):A=0}else{if(A=e[i+8>>2],!l[202976])break e;e[r+64>>2]=A,e[r+68>>2]=202976,A=202912,DA(202912,87760,r- -64|0)}return J=r+2976|0,A}function Za(A,t,r,a,s){var i,o,c=0,f=0,m=0,p=0,I=0,E=0,T=0;J=i=J-464|0,g[i+432|0]=0,g[i+368|0]=0,g[i+304|0]=0,g[i+292|0]=0,I=(0|t)/10|0,c=e[33273];e:{if(!(o=2&a)|e[33272]!=2){T=32&a?113:111,p=1&a,E=t-z(I,10)|0;A:{r:{s:{i:{c:{o:{u:{l:if(l[0|c])c=0;else{n:{if(8&a){if(e[i+288>>2]=t,DA(f=i+452|0,91198,i+288|0),c=RA(A,f,i+304|0)){f=0;break l}e[i+272>>2]=t,DA(f=i+452|0,91314,i+272|0),c=RA(A,f,i+304|0),f=0}else{if(!p)break n;if(m=qA(i+432|0,133104),4&a){if(e[i+260>>2]=T,e[i+256>>2]=t,DA(f=i+452|0,91324,i+256|0),c=RA(A,f,i+304|0),l[133116]&&c)break u;if(f=c,c)break l}e[i+244>>2]=T,e[i+240>>2]=t,DA(f=i+452|0,91384,i+240|0),f=c=RA(A,f,i+304|0)}if(c)break l}n:{if(o){if(!(1&g[133096]))break n;e[i+208>>2]=t,DA(c=i+452|0,91498,i+208|0),c=RA(A,c,i+304|0)}else m=e[A+108>>2],e[i+224>>2]=t,DA(c=i+452|0,(0|r)>=2?91700:(262144&m)>>>18|0?91534:91700,i+224|0),c=RA(A,c,i+304|0);if(c)break l}!p|!(32&l[A+109|0])?(e[i+192>>2]=t,DA(c=i+452|0,91766,i+192|0),c=RA(A,c,i+304|0)):c=0}if(!(16&a)|(0|t)>9)break c;c=f;break o}if(qA(m,133116),!(16&a)|(0|t)>9)break i}RA(A,88875,i+368|0);break r}if(!c)break s;c=f}g[i+368|0]=0;break r}s:if(p&&(e[i+180>>2]=T,e[i+176>>2]=I,DA(c=i+452|0,91846,i+176|0),RA(A,c,i+368|0))){if(f=1,!E|!(16&l[A+109|0]))break s;sa(i+368|0,133104)}else f||(e[i+160>>2]=I,DA(f=i+452|0,512&a?91936:92016,i+160|0),RA(A,f,i+368|0),f=0);if(c=E,l[i+368|0]||(c=E,16&l[A+106|0]&&(e[i+144>>2]=254&I,DA(c=i+452|0,92016,i+144|0),RA(A,c,i+368|0),c=(0|t)%20|0)),g[i+304|0]=0,I=c,(0|c)<=0)c=f;else{if(o&&(c=e[33273],l[0|c])){qA(i+304|0,c),g[i+432|0]=0,m=p;break A}if(m=0,8&a&&(e[i+128>>2]=I,DA(a=i+452|0,91314,i+128|0),m=RA(A,a,i+304|0)),!p|16&l[A+104|0]||(e[i+116>>2]=T,e[i+112>>2]=I,DA(a=i+452|0,91384,i+112|0),f=(m=RA(A,a,i+304|0))?1:f),c=f,!m){s:{if(!o|!(1&e[33274])){if(!(16&l[A+104|0])&&o)break s;f=e[A+108>>2],e[i+96>>2]=I,DA(a=i+452|0,(0|r)>=2?91700:(262144&f)>>>18|0?91534:91700,i+96|0),r=RA(A,a,i+304|0)}else e[i+80>>2]=I,DA(r=i+452|0,91498,i+80|0),r=RA(A,r,i+304|0);if(r)break r}e[i+64>>2]=I,DA(r=i+452|0,91766,i- -64|0),RA(A,r,i+304|0)}}}m=p,l[i+432|0]|c|!p||((0|t)<20|(16&l[A+104|0]?0:E)||(RA(A,92162,i+432|0),m=1,!l[i+432|0]))&&(RA(A,92205,i+432|0),m=1)}if(!(!(r=g[i+304|0])|!(48&(t=e[A+104>>2]))|!l[i+368|0])){if(RA(A,90824,i+292|0),!m|!(8&l[A+109|0])||(g[i+292|0]=0),16&l[A+104|0]){e[i+28>>2]=i+432,e[i+24>>2]=i+368,e[i+20>>2]=i+292,e[i+16>>2]=i+304,DA(s,91059,i+16|0),a=1;break e}e[i+12>>2]=i+432,e[i+8>>2]=i+304,e[i+4>>2]=i+292,e[i>>2]=i+368,DA(s,91059,i),a=1;break e}512&t&&(!r|(0|(t=HA(i+368|0)-1|0))<0||(c=l[e[144464+(g[0|(t=t+(i+368|0)|0)]<<2)>>2]+11|0]!=2,(0|(a=l[e[144464+(r<<2)>>2]+11|0]))==1&&(a=l[e[144464+(g[i+305|0]<<2)>>2]+11|0]),c|(255&a)!=2||(g[0|t]=0))),!(8&l[A+110|0])|!l[i+432|0]?(e[i+56>>2]=i+432,e[i+52>>2]=i+304,e[i+48>>2]=i+368,DA(s,92282,i+48|0)):(e[i+36>>2]=i+304,e[i+32>>2]=i+368,(0|(t=DA(s,90368,i+32|0)))>0&&(t=l[e[144464+(l[(r=t-1|0)+s|0]<<2)>>2]+11|0]==2?r:t),qA(t+s|0,i+432|0))}else qA(s,c);a=0}e:if(268435456&(A=e[A+104>>2])){if((0|HA(s))<=0)break e;for(t=0,A=0;l[0|(r=A+s|0)]==6&&(t&&(g[0|r]=5),t=1),A=A+1|0,(0|HA(s))>(0|A););}else if(256&A&&(t=0,!((0|(A=(p=HA(s))-1|0))<0))){if(A)for(E=-2&p,c=0;l[0|(f=A+s|0)]==6?(r=1,t&&(g[0|f]=5)):r=t,l[0|(f=f-1|0)]==6?(t=1,r&&(g[0|f]=5)):t=r,A=A-2|0,(0|E)!=(0|(c=c+2|0)););1&p&&(!t|l[0|(A=A+s|0)]!=6||(g[0|A]=5))}return J=i+464|0,a}function zs(A,t,r,a){var s,i,o=0,c=0,f=0,m=0,p=0,I=0,E=0;if(J=s=J-352|0,g[s+304|0]=0,g[s+224|0]=0,g[s+64|0]=0,f=e[e[47192]+292>>2],E=Gt(s+348|0,t),(1048320&(o=e[s+348>>2]))==57344&&(o&=255,e[s+348>>2]=o),2&a&&hs(o)&&RA(A,85437,s+304|0),o=yi(e[s+348>>2],A),e[s+348>>2]=o,I=1&a,jt(A,o,g[0|(i=t+E|0)],s+224|0,I),!(t=l[s+224|0])){e:if((t=I0(e[s+348>>2]))&&(e[s+348>>2]=16383&t,4&a)){switch(1073741823&(t>>=14)){case 0:case 3:break e}RA(A,t=e[131232+(t<<2)>>2],s+304|0),l[s+304|0]||(g[s+306|0]=ci(84744),p=t,t=s+304|3,RA(e[47194],p,t),l[s+307|0]&&(b[s+304>>1]=5385,t=HA(t)+(s+304|0)|0,g[t+5|0]=0,g[t+4|0]=f,g[t+3|0]=21))}jt(A,e[s+348>>2],g[0|i],s+224|0,I),t=l[s+224|0]}e:{A:{if(t&=255){if((0|t)!=21)break A;qA(r,s+224|0),E=0;break e}if(t=1632,!((0|(o=e[s+348>>2]))<1632)){for(m=103360;;){if((0|o)>=(t+10|0)){if(!(t=e[(m=m+4|0)>>2]))break A;if((0|t)<=(0|o))continue;break A}break}(0|(t=48+(o-t|0)|0))<=0||jt(A,t,0,s+224|0,I)}}A:{r:{s:{i:{if(t=Hs(e[s+348>>2])){if(o=e[t+4>>2],!t|1&(m=e[t+16>>2])||(c=e[47192],e[c+600>>2]==(0|o)|e[c+188>>2]==(0|o)|e[c+184>>2]==(0|o)||(g[s+144|0]=0,RA(c,e[t>>2],s- -64|0)?(0|(c=e[47192]))!=(0|A)&&(f=e[A+292>>2],qA(s+144|0,s- -64|0),g[s+66|0]=e[c+292>>2]):(g[s+66|0]=ci(84744),RA(e[47194],e[t>>2],s+144|0)),l[s+144|0]&&(b[s+64>>1]=5385,qA(3|(c=s- -64|0),p=s+144|0),c=HA(p)+c|0,g[c+5|0]=0,g[c+4|0]=f,g[c+3|0]=21))),l[s+224|0])break A;if(!o||(f=e[47192],e[f+188>>2]!=(0|o)))break i;t=e[f+192>>2];break r}if(l[s+224|0])break A;m=0,o=0;break s}if((t=e[t+12>>2])&&!(2&m))break r}t=25966}if((e[A+212>>2]==(0|t)&(0|t)!=27503||(g[s+226|0]=ci(Mn(s+47|0,t)),(t=e[47194])&&((0|(f=e[s+348>>2]))>55215||(0|(c=f-44032|0))<0?jt(t,f,g[0|i],s+224|3,I):(g[s+52|0]=32,t=p=s+53|0,f-50500>>>0>=588&&(t=zr(4352+((c>>>0)/588|0)|0,p)+p|0),zr(4449+(((f=(c>>>0)/28|0)>>>0)%21|0)|0,t),zr(4519+(c-z(f,28)|0)|0,t+3|0),g[t+6|0]=32,g[t+7|0]=0,g[s+227|0]=0,t=s+224|3,qt(e[47194],p,t,77,0,0,0),$t(e[47194],t,0,-1,0)),t=s+224|3,l[s+227|0]==21&&(g[s+226|0]=ci(s+224|4),jt(e[47194],e[s+348>>2],g[0|i],t,I)),ha(e[e[32972]+60>>2]),l[s+227|0]&&(b[s+224>>1]=5385,t=HA(t)+(s+224|0)|0,g[t+3|0]=21,I=e[A+292>>2],g[t+5|0]=0,g[t+4|0]=I)),!l[s+224|0]))&&(16&m||(is(e[s+348>>2])&&RA(e[47192],85683,s+224|0),l[s+224|0]||(Or(e[s+348>>2])||RA(e[47192],85778,s+224|0),l[s+224|0]||Ns(85992,s+224|0,0))),!(8&m)||4&a)){if(t=e[s+348>>2],(0|o)!=10240?(e[s+32>>2]=t,DA(s+52|0,86013,s+32|0)):(o=s+52|0,1&t&&(g[s+52|0]=49,o=s+53|0),2&t&&(g[0|o]=50,o=o+1|0),4&t&&(g[0|o]=51,o=o+1|0,t=e[s+348>>2]),8&t&&(g[0|o]=52,o=o+1|0,t=e[s+348>>2]),16&t&&(g[0|o]=53,o=o+1|0,t=e[s+348>>2]),32&t&&(g[0|o]=54,o=o+1|0,t=e[s+348>>2]),64&t&&(g[0|o]=55,o=o+1|0,t=e[s+348>>2]),128&t&&(g[0|o]=56,o=o+1|0),g[0|o]=0),t=s+224|0,m=l[s+52|0])for(o=s+52|0;t=HA(t)+t|0,g[0|t]=23,t=t+1|0,jt(e[47192],m<<24>>24,0,t,1),(a=l[0|t])&&(0|a)!=21||(0|(a=g[0|o]))<97||Ns(e[130860+((255&a)<<2)>>2],t,0),m=l[0|(o=o+1|0)];);t=HA(t)+t|0,g[0|t]=9,g[t+1|0]=0}}t=HA(r),2&l[A+144|0]?(e[s+16>>2]=255,e[s+28>>2]=s+304,e[s+24>>2]=s+224,e[s+20>>2]=s- -64,DA(s+144|0,86210,s+16|0)):(e[s>>2]=255,e[s+12>>2]=s+224,e[s+8>>2]=s+304,e[s+4>>2]=s- -64,DA(s+144|0,86210,s)),HA(s+144|0)+t>>>0>199||qA(t+r|0,s+144|0)}return J=s+352|0,E}function ws(A,t,r,a,s,i,o){var c,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0,cA=0,MA=0,zA=0,et=0,at=0,dt=0;J=c=J-528|0,ce=o?e[o>>2]:0,rA=e[s+4>>2];e:{A:{if(e[A+220>>2]>0){ar(m=c+352|0,t,160),J=T=J-176|0,Z=1-(L=e[A+220>>2])|0,ee=e[A+224>>2],he=e[A+216>>2],I=m;r:{s:{for(;;){if(ve=Gt(T+172|0,I),f=e[T+172>>2]){if((0|f)<(0|L)|(0|f)>(0|he))break s;if(ee){if((0|(f=g[ee+(f-L|0)|0]))<=0)break s}else f=f+Z|0;if(I=I+ve|0,g[E+T|0]=f,f=160,(0|(E=E+1|0))!=160)continue}else f=E;break}if(ee=0,g[f+T|0]=0,Z=g[0|T],e[T+172>>2]=Z,Z){for(cA=2+(he-L|0)|0,L=f=T;;){he=f+1|0;i:{if((ve=e[A+8180>>2])&&(E=0,!((0|(I=b[ve>>1]))>(0|(We=(g[0|he]<<8)+Z|0)))))for(;;){if((0|I)==(0|We)){Z=E+cA|0,e[T+172>>2]=Z,f=f+2|0;break i}if(!((0|We)>=(0|(I=b[ve+((E=E+1|0)<<1)>>1]))))break}f=he}if(Qe=63&Z|Qe<<6,(0|(E=ee+6|0))<8?ee=E:(ee=ee-2|0,g[0|L]=Qe>>ee,L=L+1|0),Z=g[0|f],e[T+172>>2]=Z,!Z)break}(0|ee)<=0||(g[0|L]=Qe<<8-ee,L=L+1|0)}else L=T;g[0|L]=0,wt(m,T,f=L-T|0),L=64|f;break r}L=HA(m)}J=T+176|0,T=m}else L=HA(t),T=t;if(m=l[0|T]){for(f=0,E=T;p=1023&(p=(p<<3)+m|0)^p>>>8,f=f+1|0,m=l[0|(E=E+1|0)];);f=f+p&1023}else f=0;if(p=e[692+((f<<2)+A|0)>>2]){if(f=l[0|p])break A;f=0;break e}if(f=0,!s)break e;e[s>>2]=0;break e}for(ve=1073741824&rA,We=2048&i,rA=512&ce,cA=65536&ce,MA=1&ce,zA=2&ce,ce=8&i,et=1024&i,he=4&i,at=63&L,dt=A+8233|0;;){i=(255&f)+p|0;A:{r:if((127&(f=l[p+1|0]))==(0|L)&&!xa(T,p+2|0,at)){p=2+((63&f)+p|0)|0;s:{if(f<<24>>24<0)ee=0,g[0|a]=0;else{if((0|(ee=HA(p)))>=160)break s;qA(a,p),p=1+(p+ee|0)|0}if(m=0,i>>>0<=p>>>0)f=r,I=0;else{E=0,I=0;i:{for(;;){p=(f=p)+1|0;c:if((f=l[0|f])>>>0>=100){if(Z=e[A+320>>2],f>>>0>=132){E|=Z>>>f-132&1;break c}E|=!(Z>>>f-100&1)}else{if(f>>>0>=81){Z=f-80|0,Qe=i-p|0;o:if(o)for(f=0;;){if(Ke=z(f,12)+o|0,!l[Ke+10|0])break o;if(E=!!(12&l[Ke+1|0])|E,Ke=(0|f)!=(0|Z),f=f+1|0,!Ke)break}if(h0(r,p,Qe)|1&E)break r;e[33264]=Z,f=r+Qe|0,I|=128,p=i;break i}f>>>0>=65?(I=15&f|-16&I,I=12&~f?I:512|I):f>>>0>=32?m|=1<<f-32:I|=1<<f}if(!(i>>>0>p>>>0))break}if(f=r,1&E)break A}if(65536&m&&!he||et&&49152&m)break A}if(he&&(16384&m||!ce&&32768&m)||(zA?0:512&m)|(MA?0:1024&m)|(cA?0:33554432&I)||!(!(131072&m)|ue[e[47192]+8204>>2]<=f>>>0|ve)|(rA?0:262144&m)|(8&l[e[47192]+8242|0]?0:8192&m)||16&m&&(!e[A+8184>>2]&(!ce|!e[A+8192>>2])||!(!ce|e[A+212>>2]!=25966)&&2097152&e[A+8232>>2])||(e[A+8188>>2]?0:64&m)|(!e[A+8196>>2]|We?32&m:0)||!(!(65536&I)|e[A+212>>2]!=26741|128&l[0|dt])|(e[47192]!=(0|A)?524288&m:0))break A;i:{c:{o:{if(!s){if(!ee)break o;break i}if(e[s+4>>2]=m,e[s>>2]=1073741824|I,ee)break c}if(f=0,!(8&l[188788]))break e;pn(s,A=c+272|0),e[c>>2]=t,e[c+4>>2]=A,fr(e[47195],89330,c);break e}e[s>>2]=-1073741824|I}if(8&l[188788]&&(JA(a,c- -64|0),l[e[47192]+172|0]==(I>>>29&1)&&(!o|!(128&I)?(e[c+48>>2]=t,fr(e[47195],89426,c+48|0)):(wt(A=c+352|0,a=r,r=f-r|0),g[351+(r+c|0)|0]=0,e[c+32>>2]=t,e[c+36>>2]=A,fr(e[47195],89397,c+32|0)),pn(s,A=c+272|0),t=e[47195],e[c+16>>2]=c- -64,e[c+20>>2]=A,fr(t,89534,c+16|0))),l[Gt(c- -64|0,T)+T|0]|!s||rr(e[c+64>>2]))break e;e[s>>2]=134217728|e[s>>2];break e}Pe(89236,86634,2467,94846),xe()}p=i}if(!(f=l[0|p]))break}f=0}return J=c+528|0,f}function ks(A,t,r,a,s){var i,o=0,c=0;J=i=J-304|0,g[i+278|0]=0;e:{if((0|t)>0&&(1&a&&(2&a&&(e[i+164>>2]=r,e[i+160>>2]=t,DA(o=i+290|0,89701,i+160|0),o=RA(A,o,i+224|0))||1&g[133096]&&(e[i+148>>2]=r,e[i+144>>2]=t,DA(o=i+290|0,89757,i+144|0),o=RA(A,o,i+224|0))||(e[i+132>>2]=r,e[i+128>>2]=t,DA(o=i+290|0,89894,i+128|0),o=RA(A,o,i+224|0)))||(e[i+116>>2]=r,e[i+112>>2]=t,DA(o=i+290|0,89974,i+112|0),o=RA(A,o,i+224|0))))break e;if((0|(c=(0|t)%100|0))>=20&&RA(A,90022,i+278|0),1&a){if(2&a){o=c-11|0;A:{r:{s:switch((448&e[e[47192]+108>>2])-64>>>6|0){case 0:if(o>>>0<9)break r;if(a=90418,(0|(o=(0|t)%10|0))==1)break A;if(o-2>>>0>=3)break r;a=90453;break A;case 1:if(t-2>>>0>=3)break r;a=90453;break A;case 2:if(o>>>0<9|((0|t)%10|0)-2>>>0>=3)break r;a=90453;break A;case 3:if(a=90508,o>>>0<9)break A;a=(a=(0|t)%10|0)?(0|a)==1?90453:90586:90508;break A;case 4:break s;default:break r}if(!(o>>>0<9)){if(a=90537,(0|(o=(0|t)%10|0))==1)break A;if(!(o-2>>>0>=3)){a=90453;break A}}}a=90586}if(e[i+100>>2]=r,e[i+96>>2]=a,DA(a=i+290|0,90058,i+96|0),o=0,RA(A,a,i+224|0))break e}if(a=c-11|0,1&g[133096]){A:{r:{s:switch((448&e[e[47192]+108>>2])-64>>>6|0){case 0:if(a>>>0<9)break r;if(o=90418,(0|(c=(0|t)%10|0))==1)break A;if(c-2>>>0>=3)break r;o=90453;break A;case 1:if(t-2>>>0>=3)break r;o=90453;break A;case 2:if(a>>>0<9|((0|t)%10|0)-2>>>0>=3)break r;o=90453;break A;case 3:if(o=90508,a>>>0<9)break A;o=(o=(0|t)%10|0)?(0|o)==1?90453:90586:90508;break A;case 4:break s;default:break r}if(!(a>>>0<9)){if(o=90537,(0|(c=(0|t)%10|0))==1)break A;if(!(c-2>>>0>=3)){o=90453;break A}}}o=90586}if(e[i+84>>2]=r,e[i+80>>2]=o,DA(c=i+290|0,90110,i+80|0),o=0,RA(A,c,i+224|0))break e}A:{r:{s:switch((448&e[e[47192]+108>>2])-64>>>6|0){case 0:if(a>>>0<9)break r;if(o=90418,(0|(c=(0|t)%10|0))==1)break A;if(c-2>>>0>=3)break r;o=90453;break A;case 1:if(t-2>>>0>=3)break r;o=90453;break A;case 2:if(a>>>0<9|((0|t)%10|0)-2>>>0>=3)break r;o=90453;break A;case 3:if(o=90508,a>>>0<9)break A;o=(o=(0|t)%10|0)?(0|o)==1?90453:90586:90508;break A;case 4:break s;default:break r}if(!(a>>>0<9)){if(o=90537,(0|(c=(0|t)%10|0))==1)break A;if(!(c-2>>>0>=3)){o=90453;break A}}}o=90586}if(e[i+68>>2]=r,e[i+64>>2]=o,DA(c=i+290|0,90139,i- -64|0),o=0,RA(A,c,i+224|0))break e}else a=c-11|0;A:{r:{s:switch((448&e[e[47192]+108>>2])-64>>>6|0){case 0:if(a>>>0<9)break r;if(o=90418,(0|(a=(0|t)%10|0))==1)break A;if(a-2>>>0>=3)break r;o=90453;break A;case 1:if(t-2>>>0>=3)break r;o=90453;break A;case 2:if(a>>>0<9|((0|t)%10|0)-2>>>0>=3)break r;o=90453;break A;case 3:if(o=90508,a>>>0<9)break A;o=(a=(0|t)%10|0)?(0|a)==1?90453:90586:90508;break A;case 4:break s;default:break r}if(!(a>>>0<9)){if(o=90537,(0|(a=(0|t)%10|0))==1)break A;if(!(a-2>>>0>=3)){o=90453;break A}}}o=90586}e[i+52>>2]=r,e[i+48>>2]=o,DA(a=i+290|0,90218,i+48|0),o=0,RA(A,a,i+224|0)||((0|r)<4||(e[i+32>>2]=r-1,DA(a=i+290|0,89026,i+32|0),RA(A,a,i+176|0)||(RA(A,90273,i+224|0),e[33275]=3)),l[i+224|0]||(e[i+16>>2]=t,DA(a=i+290|0,90303,i+16|0),(o=RA(A,a,i+224|0))||RA(A,90347,i+224|0),e[33275]=2))}return e[i+4>>2]=i+224,e[i>>2]=i+278,DA(s,90368,i),J=i+304|0,!((0|t)!=1|(0|r)!=1)&&(t=1,32&l[A+106|0])||(t=o),t}function Fs(A,t,r,a){var s,i=0,o=0,c=0,f=0,m=0,p=0,I=0;J=s=J+-64|0,b[s+48>>1]=0,e[s+40>>2]=0,e[s+44>>2]=0,e[s+32>>2]=0,e[s+36>>2]=0,e[s+24>>2]=0,e[s+28>>2]=0,e[s+16>>2]=0,e[s+20>>2]=0,e[s+8>>2]=0,e[s+12>>2]=0,e[s>>2]=0,e[s+4>>2]=0,i=t;e:{for(;;){A:{r:{if((0|(o=l[0|i]))!=69){if((0|o)!=32)break r;if(a&&(g[wt(o=a,t,a=(0|(a=i-t|0))>=159?159:a)+a|0]=0),a=63&r)break A;break e}g[0|i]=101}i=i+1|0;continue}break}if(1&r){A:if((i=i-1|0)>>>0<t>>>0)o=a;else for(o=a;;){if((192&l[0|i])!=128)break A;if(o=o+1|0,!((i=i-1|0)>>>0>=t>>>0))break}c=a-1|0}else c=a,o=a;if((0|a)!=1)for(;;){a=c;A:if(!((i=i-1|0)>>>0<t>>>0))for(;;){if((192&l[0|i])!=128)break A;if(o=o+1|0,!((i=i-1|0)>>>0>=t>>>0))break}A:if(!((i=i-1|0)>>>0<t>>>0))for(;;){if((192&l[0|i])!=128)break A;if(o=o+1|0,!((i=i-1|0)>>>0>=t>>>0))break}if(c=a-2|0,!((0|a)>2))break}if((0|o)<=0)c=0;else{if(f=3&(c=(a=(t=o-1|0)>>>0>=48?48:t)+1|0),t=0,o=0,a>>>0>=3)for(I=-4&c,a=0;m=i+o|0,g[o+s|0]=l[0|m],g[0|m]=32,m=(p=1|o)+i|0,g[s+p|0]=l[0|m],g[0|m]=32,m=(p=2|o)+i|0,g[s+p|0]=l[0|m],g[0|m]=32,m=(p=3|o)+i|0,g[s+p|0]=l[0|m],g[0|m]=32,o=o+4|0,(0|I)!=(0|(a=a+4|0)););if(f)for(;a=i+o|0,g[o+s|0]=l[0|a],g[0|a]=32,o=o+1|0,(0|f)!=(0|(t=t+1|0)););}}if(g[s+c|0]=0,f=65520&r,!(512&r)|l[0|(a=i-1|0)]!=105||(g[0|a]=121),o=4|f,256&r){e:{A:{r:{if((0|(t=e[A+212>>2]))!=25966){if((0|t)!=28268)break r;if(g[0|a]<0||128&(t=g[0|(c=i-2|0)]))break e;if(f=e[A+632>>2])t=!!(0|Lr(f,t));else{if((0|(f=e[A+600>>2]))>0&&(t=t-f|0)-1>>>0>254)break e;t=128&l[344+(A+t|0)|0]}if(!t)break e;if(t=g[0|a],f=e[A+612>>2])t=!!(0|Lr(f,t));else{s:{if((0|(f=e[A+600>>2]))>0){if((t=t-f|0)-1>>>0<255)break s;break e}if((0|t)<0)break e}t=4&l[344+(A+t|0)|0]}if(!t)break e;t=g[i-3|0];s:{if(f=e[A+632>>2])t=!!(0|Lr(f,t));else{i:{if((0|(f=e[A+600>>2]))>0){if((t=t-f|0)-1>>>0<255)break i;break s}if((0|t)<0)break s}t=128&l[344+(A+t|0)|0]}if(t)break e}g[0|i]=l[0|a],g[0|a]=l[0|c],g[i+1|0]=32;break e}if(c=g[i-2|0],t=e[A+632>>2])t=!!(0|Lr(t,c));else{s:{if((0|(t=e[A+600>>2]))>0){if((c=c-t|0)-1>>>0<255)break s;break A}if((0|c)<0)break A}t=128&l[344+(A+c|0)|0]}if(!t)break A;if(c=g[0|a],t=e[A+608>>2])t=!!(0|Lr(t,c));else{if((0|(t=e[A+600>>2]))>0){if((c=c-t|0)-1>>>0>=255)break A}else if((0|c)<0)break A;t=2&l[344+(A+c|0)|0]}if(!t)break A;o=xa(87771,i-3|0,3)?20|f:o;break e}o=e[A+204>>2]?20|f:o;break e}(l[0|a]==99||(c=l[1+(t=i-2|0)|0]<<8,(l[0|t]|c)==29554|(c|l[0|t])==29289||(l[0|(t=i-2|0)]|l[t+1|0]<<8)==29301||!xa(88115,i-3|0,3)||(l[0|(t=i-2|0)]|l[t+1|0]<<8)==29550|l[0|a]==117||!xa(88384,i-5|0,5)||(l[0|(t=i-4|0)]|l[t+1|0]<<8|l[t+2|0]<<16|l[t+3|0]<<24)==1735287154||(l[0|t]|l[t+1|0]<<8|l[t+2|0]<<16|l[t+3|0]<<24)==1735549292))&&(o=20|f)}16&o&&(zr(e[A+204>>2],i),8&l[188788]&&Ln(88683,6,e[47195]))}return e[A+8184>>2]|!(2048&r)||(e[A+8184>>2]=1),M[s>>1]!=115&&xa(s,88850,3)||(o|=8),J=s- -64|0,l[0|s]==39?65531&o:o}function $r(A,t,r,a,s,i,o,c,f){var m,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0;J=m=J-112|0,p=2147483647&f;e:{if(E=!(t|r),(a|(I=2147483647&s)?I-2147418112>>>0<2147549184:E)||!(!c&(0|(L=p-2147418112|0))==-2147418112?i|o:(0|L)==-2147418112&!!(0|c)|L>>>0>2147549184)){if(!(!a&(0|I)==2147418112?E:I>>>0<2147418112)){c=a,f=32768|s,i=t,o=r;break e}if(!(!c&(0|p)==2147418112?!(i|o):p>>>0<2147418112)){f|=32768;break e}if(!(t|a|2147418112^I|r)){T=a,c=(a=!(t^i|a^c|r^o|s^f^-2147483648))?0:T,f=a?2147450880:s,i=a?0:t,o=a?0:r;break e}if(!(i|c|2147418112^p|o))break e;if(!(t|a|r|I)){if(i|c|o|p)break e;i&=t,o&=r,c&=a,f&=s;break e}if(!(i|c|o|p)){i=t,o=r,c=a,f=s;break e}}I=(E=ce=(T=(0|p)==(0|I))&(0|a)==(0|c)?(0|r)==(0|o)&t>>>0<i>>>0|r>>>0<o>>>0:T&a>>>0<c>>>0|p>>>0>I>>>0)?i:t,L=E?o:r,he=T=E?f:s,E=E?c:a,ee=65535&T,a=ce?a:c,ve=s=ce?s:f,T=s>>>16&32767,(Z=he>>>16&32767)||(f=s=!(E|ee),p=s?I:E,c=s<<=6,ea(m+96|0,I,L,E,ee,(s=s+((0|(f=Ye(f?L:ee)))==32?Ye(p)+32|0:f)|0)-15|0),E=e[m+104>>2],ee=e[m+108>>2],L=e[m+100>>2],Z=16-s|0,I=e[m+96>>2]),i=ce?t:i,o=ce?r:o,c=a,f=65535&ve,T||(s=t=!(c|f),p=t?i:c,r=t<<=6,ea(m+80|0,i,o,c,f,(t=t+((0|(s=Ye(s?o:f)))==32?Ye(p)+32|0:s)|0)-15|0),T=16-t|0,c=e[m+88>>2],f=e[m+92>>2],o=e[m+84>>2],i=e[m+80>>2]),r=f<<3|c>>>29,t=c<<3|o>>>29,r|=524288,c=E<<3|L>>>29,f=ee<<3|E>>>29,ce=he^ve,s=o<<3|i>>>29,a=i<<3,(0|T)!=(0|Z)&&((i=Z-T|0)>>>0>127?(t=0,r=0,s=0,a=1):(ea(m- -64|0,a,s,t,r,128-i|0),Ya(m+48|0,a,s,t,r,i),t=e[m+56>>2],r=e[m+60>>2],s=e[m+52>>2],a=e[m+48>>2]|!!(e[m+64>>2]|e[m+72>>2]|e[m+68>>2]|e[m+76>>2]))),E=a,p=s,T=c,ee=524288|f,s=L<<3|I>>>29,L=I<<3,I=s;A:if((0|ce)<0){if(i=0,o=0,c=0,f=0,!(E^L|t^T|p^I|r^ee))break e;if(a=L-E|0,s=I-((E>>>0>L>>>0)+p|0)|0,c=(i=T-t|0)-(o=(0|p)==(0|I)&E>>>0>L>>>0|p>>>0>I>>>0)|0,f=t=(ee-((t>>>0>T>>>0)+r|0)|0)-(i>>>0<o>>>0)|0,t>>>0>524287)break A;i=t=!(c|f),o=t?a:c,r=t<<=6,ea(m+32|0,a,s,c,f,t=(t=t+((0|(i=Ye(i?s:f)))==32?Ye(o)+32|0:i)|0)-12|0),Z=Z-t|0,c=e[m+40>>2],f=e[m+44>>2],a=e[m+32>>2],s=e[m+36>>2]}else s=p+I|0,i=(0|p)==(0|(s=(a=E+L|0)>>>0<L>>>0?s+1|0:s))&a>>>0<E>>>0|s>>>0<p>>>0,p=r+ee|0,p=(t=t+T|0)>>>0<T>>>0?p+1|0:p,1048576&(f=(c=t+i|0)>>>0<t>>>0?p+1|0:p)&&(a=1&E|(1&s)<<31|a>>>1,s=c<<31|s>>>1,Z=Z+1|0,c=(1&f)<<31|c>>>1,f=f>>>1|0);if(r=0,I=-2147483648&he,(0|Z)>=32767)c=r,f=2147418112|I,i=0,o=0;else if(T=0,(0|Z)>0?T=Z:(ea(m+16|0,a,s,c,f,Z+127|0),Ya(m,a,s,c,f,1-Z|0),a=e[m>>2]|!!(e[m+16>>2]|e[m+24>>2]|e[m+20>>2]|e[m+28>>2]),s=e[m+4>>2],c=e[m+8>>2],f=e[m+12>>2]),L=7&a,a=(0|(t=c<<29|s>>>3))==(0|(o=(a=(7&s)<<29|a>>>3)>>>0>(i=(L>>>0>4)+a|0)>>>0?t+1|0:t))&a>>>0>i>>>0|t>>>0>o>>>0,t=r|(7&f)<<29|c>>>3,f=I|f>>>3&65535|T<<16,f=t>>>0>(c=a+t|0)>>>0?f+1|0:f,(0|L)!=4){if(!L)break e}else p=o+(t=0)|0,f=(t=(0|t)==(0|(o=(a=i)>>>0>(i=i+(r=1&i)|0)>>>0?p+1|0:p))&r>>>0>i>>>0|t>>>0>o>>>0)>>>0>(c=t+c|0)>>>0?f+1|0:f}e[A>>2]=i,e[A+4>>2]=o,e[A+8>>2]=c,e[A+12>>2]=f,J=m+112|0}function xt(A){var t=0,r=0,a=0,s=0,i=0,o=0,c=0;e:if(A|=0){i=(a=A-8|0)+(A=-8&(t=e[A-4>>2]))|0;A:if(!(1&t)){if(!(3&t)||(a=a-(t=e[a>>2])|0)>>>0<ue[57156])break e;if(A=A+t|0,e[57157]==(0|a)){if(!(3&~(t=e[i+4>>2])))return e[57154]=A,e[i+4>>2]=-2&t,e[a+4>>2]=1|A,void(e[A+a>>2]=A)}else{if(t>>>0<=255){if(s=e[a+8>>2],t=t>>>3|0,(0|(r=e[a+12>>2]))==(0|s)){e[57152]=e[57152]&aa(-2,t);break A}e[s+12>>2]=r,e[r+8>>2]=s;break A}if(c=e[a+24>>2],(0|a)==(0|(t=e[a+12>>2])))if((r=e[(s=a+20|0)>>2])||(r=e[(s=a+16|0)>>2])){for(;o=s,(r=e[(s=(t=r)+20|0)>>2])||(s=t+16|0,r=e[t+16>>2]););e[o>>2]=0}else t=0;else r=e[a+8>>2],e[r+12>>2]=t,e[t+8>>2]=r;if(!c)break A;s=e[a+28>>2];r:{if(e[(r=228912+(s<<2)|0)>>2]==(0|a)){if(e[r>>2]=t,t)break r;e[57153]=e[57153]&aa(-2,s);break A}if(e[c+(e[c+16>>2]==(0|a)?16:20)>>2]=t,!t)break A}if(e[t+24>>2]=c,(r=e[a+16>>2])&&(e[t+16>>2]=r,e[r+24>>2]=t),!(r=e[a+20>>2]))break A;e[t+20>>2]=r,e[r+24>>2]=t}}if(!(a>>>0>=i>>>0)&&1&(t=e[i+4>>2])){A:{if(!(2&t)){if(e[57158]==(0|i)){if(e[57158]=a,A=e[57155]+A|0,e[57155]=A,e[a+4>>2]=1|A,e[57157]!=(0|a))break e;return e[57154]=0,void(e[57157]=0)}if(e[57157]==(0|i))return e[57157]=a,A=e[57154]+A|0,e[57154]=A,e[a+4>>2]=1|A,void(e[A+a>>2]=A);A=(-8&t)+A|0;r:if(t>>>0<=255){if(s=e[i+8>>2],t=t>>>3|0,(0|(r=e[i+12>>2]))==(0|s)){e[57152]=e[57152]&aa(-2,t);break r}e[s+12>>2]=r,e[r+8>>2]=s}else{if(c=e[i+24>>2],(0|i)==(0|(t=e[i+12>>2])))if((r=e[(s=i+20|0)>>2])||(r=e[(s=i+16|0)>>2])){for(;o=s,(r=e[(s=(t=r)+20|0)>>2])||(s=t+16|0,r=e[t+16>>2]););e[o>>2]=0}else t=0;else r=e[i+8>>2],e[r+12>>2]=t,e[t+8>>2]=r;if(c){s=e[i+28>>2];s:{if(e[(r=228912+(s<<2)|0)>>2]==(0|i)){if(e[r>>2]=t,t)break s;e[57153]=e[57153]&aa(-2,s);break r}if(e[c+(e[c+16>>2]==(0|i)?16:20)>>2]=t,!t)break r}e[t+24>>2]=c,(r=e[i+16>>2])&&(e[t+16>>2]=r,e[r+24>>2]=t),(r=e[i+20>>2])&&(e[t+20>>2]=r,e[r+24>>2]=t)}}if(e[a+4>>2]=1|A,e[A+a>>2]=A,e[57157]!=(0|a))break A;return void(e[57154]=A)}e[i+4>>2]=-2&t,e[a+4>>2]=1|A,e[A+a>>2]=A}if(A>>>0<=255)return t=228648+(-8&A)|0,(r=e[57152])&(A=1<<(A>>>3))?A=e[t+8>>2]:(e[57152]=A|r,A=t),e[t+8>>2]=a,e[A+12>>2]=a,e[a+12>>2]=t,void(e[a+8>>2]=A);s=31,A>>>0<=16777215&&(s=62+((A>>>38-(t=Ye(A>>>8|0))&1)-(t<<1)|0)|0),e[a+28>>2]=s,e[a+16>>2]=0,e[a+20>>2]=0,o=228912+(s<<2)|0;A:{r:{if((r=e[57153])&(t=1<<s)){for(s=A<<((0|s)!=31?25-(s>>>1|0):0),t=e[o>>2];;){if(r=t,(-8&e[t+4>>2])==(0|A))break r;if(t=s>>>29|0,s<<=1,!(t=e[16+(o=r+(4&t)|0)>>2]))break}e[o+16>>2]=a,e[a+24>>2]=r}else e[57153]=t|r,e[o>>2]=a,e[a+24>>2]=o;e[a+12>>2]=a,e[a+8>>2]=a;break A}A=e[r+8>>2],e[A+12>>2]=a,e[r+8>>2]=a,e[a+24>>2]=0,e[a+12>>2]=r,e[a+8>>2]=A}A=e[57160]-1|0,e[57160]=A||-1}}}function Ea(A,t,r){var a,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0;J=a=J-176|0;e:{if(32&t)T=r-((0|r)>1)|0;else{i=Ut(A,93302);A:{r:{s:{if((0|t)!=2){if(i)break s;t=0;break e}if(T=r+1|0,I=56+(L=z(r,76)+133152|0)|0,E=Ut(A,89360),o=Ut(A,93318),c=Ut(A,93426),f=Ut(A,93499),i)break r;break A}T=r+1|0,I=56+(L=z(r,76)+133152|0)|0,f=0}for(A=0,m=(0|(s=e[i-4>>2]))!=34?(0|s)==39?s:0:s,Z=z(r,76)+133208|0;;){if(s=A,!(A=e[i>>2]))break A;r:{if(!m){if((0|A)==32|A-9>>>0<5)break A;if((0|A)!=47)break r;break A}if((0|s)!=92&&(0|A)==(0|m))break A}if(i=i+4|0,!((0|(p=zr(A,p+Z|0)+p|0))<16))break}}m=0,g[p+I|0]=0,p=z(r,76)+133168|0,i=0;A:if(E)for(A=0,I=(0|(s=e[E-4>>2]))!=34?(0|s)==39?s:0:s;;){if(s=A,!(A=e[E>>2]))break A;r:{if(!I){if((0|A)==32|A-9>>>0<5)break A;if((0|A)!=47)break r;break A}if((0|s)!=92&&(0|A)==(0|I))break A}if(E=E+4|0,!((0|(i=zr(A,i+p|0)+i|0))<36))break}if(g[i+p|0]=0,!(!o|e[o>>2]-48>>>0>=10)){for(;m=(e[o>>2]+z(m,10)|0)-48|0,e[(o=o+4|0)>>2]-48>>>0<10;);(0|m)<=0||(m=m-1|0)}if(s=z(r,76)+133152|0,e[s+4>>2]=m,A=0,i=0,!(!c|e[c>>2]-48>>>0>=10))for(;i=(e[c>>2]+z(i,10)|0)-48|0,e[(c=c+4|0)>>2]-48>>>0<10;);e[s+12>>2]=i,i=z(r,76)+133152|0;A:{r:if(f){for(;r=g[A+93099|0],(s=e[(A<<2)+f>>2])&&(A=A+1|0,(0|r)==(0|s)););s:{i:switch(s-34|0){case 0:case 5:break i;default:break s}if(!r){A=0;break A}}for(A=0;r=g[A+93116|0],(s=e[(A<<2)+f>>2])&&(A=A+1|0,(0|r)==(0|s)););s:{i:switch(s-34|0){case 0:case 5:break i;default:break s}if(!r){A=1;break A}}for(A=0;r=g[A+93197|0],(s=e[(A<<2)+f>>2])&&(A=A+1|0,(0|r)==(0|s)););s:switch(s-34|0){case 0:case 5:break s;default:break r}if(!r){A=2;break A}}A=3}e[i+8>>2]=e[131156+(A<<3)>>2],e[L>>2]=t}if(qA(137776,133168),r=qA(a+96|0,133208),g[a+157|0]=e[33291],g[a+156|0]=e[33290],A=e[33289],e[a+152>>2]=0,g[a+158|0]=A,(0|T)>0)for(o=0;;){if(c=1,t=z(o,76)+133152|0,l[0|(A=t+16|0)]&&pa(0,A)&&(qA(137776,A),c=0,g[0|r]=0,g[a+158|0]=0,b[a+156>>1]=0),l[0|(A=t+56|0)]){f=qA(r,A),s=e[33679];A:if(l[0|(A=s)])for(;;){if(!vr(A=A+1|0,f)){qA(f,s+1|0);break A}if(A=1+(HA(A)+A|0)|0,!l[0|A])break}c&&(g[137776]=0)}if((A=e[t+8>>2])&&(g[a+156|0]=A),(A=e[t+12>>2])&&(g[a+157|0]=A),(A=e[t+4>>2])&&(g[a+158|0]=A),(0|T)==(0|(o=o+1|0)))break}e[a+148>>2]=r,e[a+144>>2]=137776,(A=cs(a+144|0,a+172|0))?$a(A,43)||(t=l[a+156|0],!l[134672]|((0|t)!=l[134724]?t:0)||(e[a>>2]=A,e[a+4>>2]=134672,DA(t=a+16|0,93533,a),A=137776,ar(137776,t,40))):A=92003,t=0,vr(A,134784)&&(qA(134784,A),t=131072)}return J=a+176|0,t}function Tr(A,t){var r,a=0,s=0,i=0,o=0,c=0;r=A+t|0;e:{A:if(!(1&(a=e[A+4>>2]))){if(!(3&a))break e;t=(a=e[A>>2])+t|0;r:{if((0|(A=A-a|0))!=e[57157]){if(a>>>0<=255){if(i=e[A+8>>2],a=a>>>3|0,(0|(s=e[A+12>>2]))!=(0|i))break r;e[57152]=e[57152]&aa(-2,a);break A}if(c=e[A+24>>2],(0|(a=e[A+12>>2]))==(0|A))if((s=e[(i=A+20|0)>>2])||(s=e[(i=A+16|0)>>2])){for(;o=i,(s=e[(i=(a=s)+20|0)>>2])||(i=a+16|0,s=e[a+16>>2]););e[o>>2]=0}else a=0;else s=e[A+8>>2],e[s+12>>2]=a,e[a+8>>2]=s;if(!c)break A;i=e[A+28>>2];s:{if(e[(s=228912+(i<<2)|0)>>2]==(0|A)){if(e[s>>2]=a,a)break s;e[57153]=e[57153]&aa(-2,i);break A}if(e[c+(e[c+16>>2]==(0|A)?16:20)>>2]=a,!a)break A}if(e[a+24>>2]=c,(s=e[A+16>>2])&&(e[a+16>>2]=s,e[s+24>>2]=a),!(s=e[A+20>>2]))break A;e[a+20>>2]=s,e[s+24>>2]=a;break A}if(3&~(a=e[r+4>>2]))break A;return e[57154]=t,e[r+4>>2]=-2&a,e[A+4>>2]=1|t,void(e[r>>2]=t)}e[i+12>>2]=s,e[s+8>>2]=i}A:{if(!(2&(a=e[r+4>>2]))){if(e[57158]==(0|r)){if(e[57158]=A,t=e[57155]+t|0,e[57155]=t,e[A+4>>2]=1|t,e[57157]!=(0|A))break e;return e[57154]=0,void(e[57157]=0)}if(e[57157]==(0|r))return e[57157]=A,t=e[57154]+t|0,e[57154]=t,e[A+4>>2]=1|t,void(e[A+t>>2]=t);t=(-8&a)+t|0;r:if(a>>>0<=255){if(i=e[r+8>>2],a=a>>>3|0,(0|(s=e[r+12>>2]))==(0|i)){e[57152]=e[57152]&aa(-2,a);break r}e[i+12>>2]=s,e[s+8>>2]=i}else{if(c=e[r+24>>2],(0|r)==(0|(a=e[r+12>>2])))if((i=e[(s=r+20|0)>>2])||(i=e[(s=r+16|0)>>2])){for(;o=s,(i=e[(s=(a=i)+20|0)>>2])||(s=a+16|0,i=e[a+16>>2]););e[o>>2]=0}else a=0;else s=e[r+8>>2],e[s+12>>2]=a,e[a+8>>2]=s;if(c){i=e[r+28>>2];s:{if(e[(s=228912+(i<<2)|0)>>2]==(0|r)){if(e[s>>2]=a,a)break s;e[57153]=e[57153]&aa(-2,i);break r}if(e[c+(e[c+16>>2]==(0|r)?16:20)>>2]=a,!a)break r}e[a+24>>2]=c,(s=e[r+16>>2])&&(e[a+16>>2]=s,e[s+24>>2]=a),(s=e[r+20>>2])&&(e[a+20>>2]=s,e[s+24>>2]=a)}}if(e[A+4>>2]=1|t,e[A+t>>2]=t,e[57157]!=(0|A))break A;return void(e[57154]=t)}e[r+4>>2]=-2&a,e[A+4>>2]=1|t,e[A+t>>2]=t}if(t>>>0<=255)return a=228648+(-8&t)|0,(s=e[57152])&(t=1<<(t>>>3))?t=e[a+8>>2]:(e[57152]=t|s,t=a),e[a+8>>2]=A,e[t+12>>2]=A,e[A+12>>2]=a,void(e[A+8>>2]=t);i=31,t>>>0<=16777215&&(i=62+((t>>>38-(a=Ye(t>>>8|0))&1)-(a<<1)|0)|0),e[A+28>>2]=i,e[A+16>>2]=0,e[A+20>>2]=0,o=228912+(i<<2)|0;A:{if((s=e[57153])&(a=1<<i)){for(i=t<<((0|i)!=31?25-(i>>>1|0):0),a=e[o>>2];;){if(s=a,(-8&e[a+4>>2])==(0|t))break A;if(a=i>>>29|0,i<<=1,!(a=e[16+(o=s+(4&a)|0)>>2]))break}e[o+16>>2]=A,e[A+24>>2]=s}else e[57153]=a|s,e[o>>2]=A,e[A+24>>2]=o;return e[A+12>>2]=A,void(e[A+8>>2]=A)}t=e[s+8>>2],e[t+12>>2]=A,e[s+8>>2]=A,e[A+24>>2]=0,e[A+12>>2]=s,e[A+8>>2]=t}}function Hr(A){var t=0,r=0,a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0;if(!(t=g[88105]))return A;if(A=$a(A,t)){if(!l[88106])return A;if(l[A+1|0]){if(!l[88107]){s=!!(0|(r=l[A+1|0]));e:if(r&&(0|(t=r|l[0|A]<<8))!=(0|(c=l[88106]|l[88105]<<8)))for(r=A+1|0;;){if(s=!!(0|(a=l[(A=r)+1|0])),!a)break e;if(r=A+1|0,(0|c)==(0|(t=a|t<<8&65280)))break}return s?A:0}if(l[A+2|0]){if(!l[88108]){r=A+2|0,s=!!(0|(t=l[A+2|0]));e:if(t&&(0|(t=l[A+1|0]<<16|l[0|A]<<24|t<<8))!=(0|(c=l[88106]<<16|l[88105]<<24|l[88107]<<8)))for(;;){if(A=r+1|0,s=!!(0|(a=l[r+1|0])),!a)break e;if(r=A,(0|c)==(0|(t=(t|a)<<8)))break}else A=r;return s?A-2|0:0}if(l[A+3|0]){if(!l[88109]){r=A+3|0,s=!!(0|(t=l[A+3|0]));e:if(t&&(0|(t=t|l[A+1|0]<<16|l[0|A]<<24|l[A+2|0]<<8))!=(0|(c=(A=l[88105]|l[88106]<<8|l[88107]<<16|l[88108]<<24)<<24|(65280&A)<<8|A>>>8&65280|A>>>24)))for(;;){if(A=r+1|0,s=!!(0|(a=l[r+1|0])),!a)break e;if(r=A,(0|c)==(0|(t=a|t<<8)))break}else A=r;return s?A-3|0:0}c=A,J=f=J-1056|0,e[(A=f+1048|0)>>2]=0,e[A+4>>2]=0,e[(A=f+1040|0)>>2]=0,e[A+4>>2]=0,e[f+1032>>2]=0,e[f+1036>>2]=0,e[f+1024>>2]=0,e[f+1028>>2]=0;e:{A:{r:{s:{if(t=l[88105]){for(;;){if(!l[i+c|0])break A;if(i=i+1|0,e[((255&t)<<2)+f>>2]=i,e[(A=(f+1024|0)+(t>>>3&28)|0)>>2]=e[A>>2]|1<<t,!(t=l[i+88105|0]))break}if(A=1,m=-1,i>>>0>1)break s}else m=-1,A=1;o=-1,r=1;break r}for(a=1,t=1;;){s:if((0|(o=l[88105+(t+m|0)|0]))!=(0|(s=l[A+88105|0])))s>>>0<o>>>0?(a=A-m|0,r=A,t=1):(m=r,r=r+1|0,a=1,t=1);else{if((0|t)==(0|a)){r=r+a|0,t=1;break s}t=t+1|0}if(!(i>>>0>(A=r+t|0)>>>0))break}if(r=1,o=-1,i>>>0<=1)A=a;else{for(A=0,s=1,t=1;;){s:if((0|(I=l[88105+(t+o|0)|0]))!=(0|(p=l[r+88105|0])))p>>>0>I>>>0?(s=r-o|0,A=r,t=1):(o=A,A=A+1|0,s=1,t=1);else{if((0|t)==(0|s)){A=A+s|0,t=1;break s}t=t+1|0}if(!(i>>>0>(r=A+t|0)>>>0))break}A=a,r=s}}for(t=A,xa(88105,(a=(A=o+1>>>0>m+1>>>0)?r:t)+88105|0,I=(p=A?o:m)+1|0)?(a=((A=~p+i|0)>>>0<p>>>0?p:A)+1|0,s=0):s=i-a|0,T=i-1|0,E=63|i,o=0,A=c;;){if(!(c-A>>>0>=i>>>0))if(r=ri(c,0,E)){if(c=r,r-A>>>0<i>>>0)break A}else c=c+E|0;r=l[A+T|0],t=i;r:{if(e[(f+1024|0)+(r>>>3&28)>>2]>>>r&1)if((0|(r=e[(r<<2)+f>>2]))==(0|i)){s:{if(m=l[(r=(t=I)>>>0>o>>>0?t:o)+88105|0])for(;;){if(l[A+r|0]!=(255&m))break s;if(!(m=l[88105+(r=r+1|0)|0]))break}for(;;){if(t>>>0<=o>>>0)break e;if(l[88105+(t=t-1|0)|0]!=l[A+t|0])break}t=a,o=s;break r}t=r-p|0}else t=(r=i-r|0)>>>0>o>>>0?r:o;o=0}A=A+t|0}}A=0}J=f+1056|0,r=A}}}}return r}function gs(A,t,r,a,s,i){var o,c,f,m,p=0,I=0,E=0;if(J=o=J-560|0,g[o+448|0]=0,g[o+144|0]=0,g[o+120|0]=0,f=34&i,c=t-z(I=(0|t)/100|0,100)|0,1&(m=64&l[A+106|0]?(0|t)>999|i:0)|(0|t)>99){e:{A:{r:{if(!(!f|c)){if(!RA(A,90606,o+304|0))break r;break e}if(c)break A}if(RA(A,90691,o+304|0))break e}RA(A,90725,o+304|0)}p=i,(0|t)<1e3||(p=i,!(8&l[A+105|0])|t-2e3>>>0<4294967196&&(g[o+208|0]=0,ks(A,p=(I>>>0)/10|0,a=16384&e[A+108>>2]?0:s+1|0,!((t>>>0)%1e3|0)|f,o+272|0)||Za(A,p,s,e[A+212>>2]==28012?520:(0|a)<4?(e[A+108>>2]>>>a&1)<<3:0,o+208|0),2&l[A+109|0]?(e[o+108>>2]=15,e[o+100>>2]=15,e[o+104>>2]=o+208,e[o+96>>2]=o+272,DA(o+144|0,90761,o+96|0)):(e[o+92>>2]=15,e[o+84>>2]=15,e[o+88>>2]=o+272,e[o+80>>2]=o+208,DA(o+144|0,90761,o+80|0)),a=1,1&(!!(0|(I=I-z(p,10)|0))|m)||(g[o+304|0]=0),p=1|i)),g[o+208|0]=0;e:if(!(~m&(0|I)<=0))if(!(4&l[A+106|0])|!(1&p|l[o+144|0])||RA(A,90824,o+120|0),!f|(16&l[A+109|0]?0:c)||(e[o+64>>2]=I,DA(a=o+548|0,90875,o- -64|0),E=RA(A,a,o+208|0),!(4096&e[A+108>>2])|(0|c)<=0||sa(o+208|0,133104)),a=1,1&(~m|!!(0|I))){A:{r:{s:{i:{if(!(131072&e[A+108>>2])||1&p|(0|I)!=1){if(c|E||(e[o+48>>2]=I,DA(a=o+548|0,90985,o+48|0),E=RA(A,a,o+208|0)),E||(e[o+32>>2]=I,DA(a=o+548|0,91027,o+32|0),RA(A,a,o+208|0)))break i;if((0|I)!=1)break r;break s}if(!E)break s}g[o+304|0]=0;break A}if(a=1,4&l[A+105|0])break e}Za(A,I,s,0,o+208|0)}a=1}else RA(A,88875,o+208|0);e[o+28>>2]=o+304,e[o+24>>2]=o+208,e[o+20>>2]=o+120,e[o+16>>2]=o+144,DA(o+448|0,91059,o+16|0)}else p=i;g[o+132|0]=0;e:{A:{if((0|c)>0)16&l[A+109|0]&&2&p||(!(1&p)|s&&(0|t)<=100||!(64&(a=e[A+104>>2]))&(!(8388608&a)|c>>>0>9)||RA(A,90824,o+132|0),!(1&p|l[o+144|0])|!(524288&e[A+104>>2])|I||RA(A,90824,o+132|0)),g[o+336|0]=0;else if(g[o+336|0]=0,!c&a)break A;if(s?(i=e[A+108>>2],t=(0|s)<4?(i>>>s&1)<<3:0):(a=f?3:2,t=32&i|((0|t)<100?1&p?a:4|a:a),i=e[A+108>>2]),t=(0|s)==1&&e[A+212>>2]==28012?520|t:t,1048576&i&&(a=16|t,t=(0|I)>0||1&p?a:t),!Za(A,c,s,256&p|t,o+336|0)|!(128&l[A+104|0]))break e;g[o+132|0]=0;break e}l[133104]&&((0|(A=HA(o+448|0)))<=0||l[447+(A=A+o|0)|0]==10&&(g[A+447|0]=0),qA(o+336|0,133104))}e[o+8>>2]=15,e[o+12>>2]=o+336,e[o+4>>2]=o+132,e[o>>2]=o+448,DA(r,91101,o),J=o+560|0}function Kt(A){var t,r,a,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0;for(s=e[32972],e[s+64>>2]=290816,e[s+68>>2]=4104,e[s+96>>2]=0,e[s+100>>2]=0,e[s+120>>2]=90,e[s+124>>2]=100,e[s+112>>2]=64,e[s+116>>2]=256,e[s+104>>2]=5,e[s+108>>2]=0,e[s+84>>2]=100,e[s+88>>2]=64,i=e[50754],e[s+132>>2]=0,e[s+136>>2]=0,e[s+128>>2]=i,e[s+140>>2]=0,e[s+144>>2]=0,e[s+148>>2]=0,e[s+152>>2]=0,e[s+156>>2]=0,e[s+160>>2]=0,e[36435]=450,e[s+92>>2]=2,e[50870]=0,e[50871]=0,e[50872]=0,e[50873]=0,e[50880]=0,e[50881]=0,e[50882]=0,e[50883]=0,e[50890]=0,e[50891]=0,e[50892]=0,e[50893]=0,o=-3.141592653589793/+e[50754],D[25429]=o,f=-2*o,D[25430]=f,t=(o=Pt(200*o))*-o,D[25434]=t,D[25439]=t,D[25444]=t,o=(f=o*Ga(2e3*f))+f,D[25433]=o,D[25438]=o,D[25443]=o,f=1-o-t,D[25432]=f,D[25437]=f,D[25442]=f,e[50902]=0,e[50903]=0,e[50900]=0,e[50901]=0,D[25449]=t,D[25448]=o,e[50912]=0,e[50913]=0,D[25447]=f,e[50910]=0,e[50911]=0,D[25454]=t,D[25453]=o,e[50922]=0,e[50923]=0,D[25452]=f,e[50920]=0,e[50921]=0,D[25459]=t,D[25458]=o,e[50932]=0,e[50933]=0,D[25457]=f,e[50930]=0,e[50931]=0,D[25464]=t,D[25463]=o,D[25462]=f,e[50942]=0,e[50943]=0,e[50940]=0,e[50941]=0,D[25469]=t,D[25468]=o,D[25467]=f,e[50952]=0,e[50953]=0,e[50950]=0,e[50951]=0,D[25474]=t,D[25473]=o,D[25472]=f,r=e[32972],i=0;b[236+(m=(i<<1)+r|0)>>1]=256,b[m+164>>1]=256,s=l[i+105376|0]<<1,b[m+254>>1]=s,b[m+182>>1]=s,b[m+200>>1]=l[i+105385|0]<<1,e[308+(s=(c=i<<2)+r|0)>>2]=e[c+105408>>2],e[s+272>>2]=0,b[m+218>>1]=0,e[c+200944>>2]=(0|z(e[c+105456>>2],22050))/e[50754],(0|(i=i+1|0))!=9;);for(T=e[32961],i=0,a=r+344|0;;){if(s=p,I=T,m=i,(0|(p=e[(i=131840+(c=i<<2)|0)>>2]))==-1&&(p=8e3,e[i>>2]=8e3,m&&(e[131840+(4|c)>>2]=e[c+131836>>2])),T=e[131840+(4|c)>>2],!((0|s)>=(0|(p=(0|p)/8|0))||(0|(E=p-s|0))<=0||(c=s+1|0,i=s,1&E&&(g[344+(s+r|0)|0]=(0|I)>=255?255:I,i=c),(0|c)==(0|p))))for(L=T-I|0;c=((0|z(i-s|0,L))/(0|E)|0)+I|0,g[i+a|0]=(0|c)>=255?255:c,Z=((0|z((c=i+1|0)-s|0,L))/(0|E)|0)+I|0,g[c+a|0]=(0|Z)>=255?255:Z,(0|p)!=(0|(i=i+2|0)););if(i=m+2|0,!(m>>>0<10))break}i=e[32972],e[i+80>>2]=232,e[i+72>>2]=256,e[i+76>>2]=238,A||(e[49848]=0),b[i+200>>1]=(0|z(b[i+200>>1],105))/100}function Xa(A,t,r){var a,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0;J=a=J-288|0,(0|t)!=132848&&Rr(132848,t,40),(0|(s=A+228|0))!=(0|t)&&Rr(s,t,40),e[a+88>>2]=t,e[a+84>>2]=47,e[a+80>>2]=137584,DA(s=a+96|0,84089,a+80|0),i=ss(s),(s=e[A+688>>2])&&(xt(s),e[A+688>>2]=0),s=wa(a+96|0,84577);e:if((0|i)>0&&s)if(r=Ct(i),e[A+688>>2]=r,r)if(m=mn(r,i,s),ta(s),m>>>0<=1032)e[a+16>>2]=a+96,fr(e[30450],85164,a+16|0),r=2;else if(s=e[A+688>>2],r=e[s+4>>2],!((0|(i=e[s>>2]))!=1024|(0|r)<=0)&(0|r)<134217729){s=r+s|0,e[A+684>>2]=s,I=jA(A+5168|0,0,1024),jA(A+7664|0,0,260),jA(A+7924|0,255,256),jA(A+4788|0,0,380),jA(A+6192|0,0,512);A:if((0|(r=l[0|s]))!=7)for(;;){if((0|(i=255&r))!=6){if(!i)break A;i=e[A+684>>2],e[a+72>>2]=r<<24>>24,e[a+64>>2]=132848,e[a+68>>2]=s-i,fr(e[30450],88950,a- -64|0);break}r:{s:{i:{c:{o:switch((i=l[0|(r=s+1|0)])-18|0){case 0:break c;case 2:break o;default:break i}for(r=4+(-4&r)|0,e[A+180>>2]=r;r=(s=r)+1|0,!Hn(s););for(;i=l[0|s],r=s,s=s+1|0,(0|i)!=7;);break r}if(r=s+3|0,(0|(s=((0|(s=g[s+2|0]))<65?191:-65)+s|0))>94)break s;e[4788+((s<<2)+A|0)>>2]=r;break s}switch(r=1+((c=HA(r))+r|0)|0,0|c){case 1:e[5168+((i<<2)+A|0)>>2]=r;break s;case 0:e[I>>2]=r;break s}c=l[s+2|0],(0|i)!=1?(s=e[A+7664>>2],l[0|(p=7924+(o=A+i|0)|0)]==255&&(g[0|p]=s),g[0|(o=o+7668|0)]=l[0|o]+1,e[6704+(o=(s<<2)+A|0)>>2]=r,e[A+7664>>2]=s+1,e[o+7184>>2]=i|c<<8):e[6188+((c<<2)+A|0)>>2]=r}if(l[0|r]!=7)for(;r=1+(HA(r)+r|0)|0,l[0|r]!=7;);}r=l[0|(s=r+1|0)]}for(s=e[A+688>>2]+8|0;;){for(e[692+((r=f<<2)+A|0)>>2]=s;i=l[0|s];)s=s+i|0;for(s=s+1|0,e[692+((4|r)+A|0)>>2]=s;r=l[0|s];)s=r+s|0;if(s=s+1|0,(0|(f=f+2|0))==1024)break}r=0,(0|(A=e[A+324>>2]))<=0|A>>>0<=m>>>0||(e[a+48>>2]=t,fr(e[30450],85519,a+48|0))}else e[a+40>>2]=r,e[a+36>>2]=i,e[a+32>>2]=a+96,fr(e[30450],85349,a+32|0),r=2;else ta(s),r=3;else{if(r||(e[a>>2]=a+96,fr(e[30450],84963,a)),r=1,!s)break e;ta(s)}return J=a+288|0,r}function us(A){var t,r=0,a=0;for(t=jA(A+344|0,0,256),g[A+364|0]=1,g[A+356|0]=1,g[A+357|0]=1,g[A+358|0]=1,g[A+359|0]=1,g[A+360|0]=1,g[A+361|0]=1,g[A+362|0]=1,g[A+363|0]=1,g[A+348|0]=1,g[A+349|0]=1,g[A+350|0]=1,g[A+351|0]=1,g[A+352|0]=1,g[A+353|0]=1,g[A+354|0]=1,g[A+355|0]=1,g[A+431|0]=3,g[A+429|0]=3,g[A+430|0]=3,g[A+406|0]=3,g[A+407|0]=3,g[A+408|0]=3,g[A+409|0]=3,g[A+410|0]=3,g[A+411|0]=3,g[A+412|0]=3,g[A+413|0]=3,g[A+414|0]=3,g[A+415|0]=3,g[A+416|0]=3,g[A+417|0]=3,g[A+418|0]=3,g[A+419|0]=3,g[A+420|0]=3,g[A+421|0]=3,g[A+440|0]=3,g[A+441|0]=3,g[A+442|0]=3,g[A+443|0]=3,a=21;g[344+(r=A+a|0)|0]=4|l[r+344|0],(0|(r=a+1|0))!=58;)g[0|(r=r+t|0)]=4|l[0|r],g[2+(r=a+t|0)|0]=4|l[r+2|0],g[r+3|0]=4|l[r+3|0],a=a+4|0;g[A+346|0]=4|l[A+346|0],g[A+347|0]=4|l[A+347|0],g[A+432|0]=4|l[A+432|0],g[A+433|0]=4|l[A+433|0],g[A+434|0]=4|l[A+434|0],g[A+435|0]=4|l[A+435|0],g[A+436|0]=4|l[A+436|0],g[A+437|0]=4|l[A+437|0],g[A+438|0]=4|l[A+438|0],g[A+439|0]=4|l[A+439|0],g[A+467|0]=4|l[A+467|0],g[A+468|0]=4|l[A+468|0],g[A+470|0]=4|l[A+470|0],g[A+471|0]=4|l[A+471|0],g[A+348|0]=64|l[A+348|0],g[A+349|0]=64|l[A+349|0],g[A+350|0]=64|l[A+350|0],g[A+351|0]=64|l[A+351|0],g[A+352|0]=64|l[A+352|0],g[A+353|0]=64|l[A+353|0],g[A+354|0]=64|l[A+354|0],g[A+355|0]=64|l[A+355|0],g[A+356|0]=64|l[A+356|0],g[A+357|0]=64|l[A+357|0],g[A+358|0]=64|l[A+358|0],g[A+359|0]=64|l[A+359|0],g[A+360|0]=64|l[A+360|0],g[A+361|0]=64|l[A+361|0],g[A+362|0]=64|l[A+362|0],g[A+363|0]=64|l[A+363|0],g[A+364|0]=64|l[A+364|0],g[A+406|0]=64|l[A+406|0],g[A+407|0]=64|l[A+407|0],g[A+408|0]=64|l[A+408|0],g[A+409|0]=64|l[A+409|0],g[A+410|0]=64|l[A+410|0],g[A+411|0]=64|l[A+411|0],g[A+412|0]=64|l[A+412|0],g[A+413|0]=64|l[A+413|0],g[A+414|0]=64|l[A+414|0],g[A+415|0]=64|l[A+415|0],g[A+416|0]=64|l[A+416|0],g[A+417|0]=64|l[A+417|0],g[A+418|0]=64|l[A+418|0],g[A+419|0]=64|l[A+419|0],g[A+420|0]=64|l[A+420|0],g[A+440|0]=64|l[A+440|0],g[A+441|0]=64|l[A+441|0],g[A+429|0]=64|l[A+429|0],g[A+430|0]=64|l[A+430|0],g[A+431|0]=64|l[A+431|0],g[A+442|0]=64|l[A+442|0],g[A+443|0]=64|l[A+443|0],e[A+40>>2]=1,e[A+204>>2]=e[A+600>>2]+77}function hi(A,t,r,a,s,i,o,c,f){var m,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0;J=m=J-128|0;e:{A:{if(gA(i,o,c,f,0,0,0,0)){I=65535&f;r:s:{if((0|(T=f>>>16&32767))!=32767){if(p=4,T)break s;p=i|c|o|I?3:2;break r}p=!(i|c|o|I)}if((0|(L=32767&(he=s>>>16|0)))!=32767&&p)break A}PA(m+16|0,t,r,a,s,i,o,c,f),Qr(m,t=e[m+16>>2],a=e[m+20>>2],s=e[m+24>>2],r=e[m+28>>2],t,a,s,r),a=e[m+8>>2],s=e[m+12>>2],c=e[m>>2],f=e[m+4>>2];break e}if(T=c,(0|gA(t,r,p=a,Z=2147483647&s,i,o,c,E=2147483647&f))<=0){if(gA(t,r,p,Z,i,o,T,E)){c=t,f=r;break e}PA(m+112|0,t,r,a,s,0,0,0,0),a=e[m+120>>2],s=e[m+124>>2],c=e[m+112>>2],f=e[m+116>>2]}else{if(ee=f>>>16&32767,L?(f=r,c=t):(PA(m+96|0,t,r,p,Z,0,0,0,1081540608),p=e[m+104>>2],Z=c=e[m+108>>2],L=(c>>>16|0)-120|0,f=e[m+100>>2],c=e[m+96>>2]),ee||(PA(m+80|0,i,o,T,E,0,0,0,1081540608),T=e[m+88>>2],E=i=e[m+92>>2],ee=(i>>>16|0)-120|0,o=e[m+84>>2],i=e[m+80>>2]),ce=T,ve=65535&E|65536,Z=65535&Z|65536,(0|L)>(0|ee)){for(;;){if(T=(E=p-ce|0)-(I=(0|o)==(0|f)&i>>>0>c>>>0|o>>>0>f>>>0)|0,(0|(I=(Z-((p>>>0<ce>>>0)+ve|0)|0)-(I>>>0>E>>>0)|0))>0|(0|I)>=0){if(p=c,!((c=c-i|0)|T|(f=f-((i>>>0>p>>>0)+o|0)|0)|I)){PA(m+32|0,t,r,a,s,0,0,0,0),a=e[m+40>>2],s=e[m+44>>2],c=e[m+32>>2],f=e[m+36>>2];break e}I=I<<1|T>>>31,p=T<<1|f>>>31}else I=Z<<1|p>>>31,p=p<<1|f>>>31;if(Z=I,I=f<<1|c>>>31,c<<=1,f=I,!((0|(L=L-1|0))>(0|ee)))break}L=ee}if(T=(E=p-ce|0)-(I=(0|o)==(0|f)&i>>>0>c>>>0|o>>>0>f>>>0)|0,E=I=(Z-((p>>>0<ce>>>0)+ve|0)|0)-(I>>>0>E>>>0)|0,(0|I)<0)T=p,E=Z;else if(p=c,!((c=c-i|0)|T|(f=f-((i>>>0>p>>>0)+o|0)|0)|E)){PA(m+48|0,t,r,a,s,0,0,0,0),a=e[m+56>>2],s=e[m+60>>2],c=e[m+48>>2],f=e[m+52>>2];break e}if((0|E)==65535|E>>>0<65535)for(;t=f>>>31|0,L=L-1|0,Z=f<<1|c>>>31,c<<=1,f=Z,r=t,t=E<<1|T>>>31,T=r|T<<1,E=t,t>>>0<65536;);t=32768&he,(0|L)<=0?(PA(m- -64|0,c,f,T,65535&E|(t|L+120)<<16,0,0,0,1065811968),a=e[m+72>>2],s=e[m+76>>2],c=e[m+64>>2],f=e[m+68>>2]):(a=T,s=65535&E|(t|L)<<16)}}e[A>>2]=c,e[A+4>>2]=f,e[A+8>>2]=a,e[A+12>>2]=s,J=m+128|0}function _s(A,t,r){var a,s,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0;if(J=a=J-1040|0,(0|(o=p0(A,589824,0)))>=0&&((i=x0(1,2072))?e[i+8>>2]=o:(GA(0|o),i=0)),s=i,i){e:if(i=u0(s))for(T=e[30450],E=(a+96|0)+t|0;;){if((0|(o=e[50303]))>=348){e[a+4>>2]=350,e[a>>2]=o+1,fr(T,91860,a);break e}if(l[i+19|0]!=46)if(e[a+88>>2]=i+19,e[a+84>>2]=47,e[a+80>>2]=A,DA(i=a+96|0,91924,a+80|0),(0|(i=ss(i)))!=-31){if(!((0|i)<=0)&&(I=wa(a+96|0,85712))){c=0,g[a+832|0]=0,g[a+752|0]=0,e[a+360>>2]=0,e[a+356>>2]=4,m=0;A:for(;;){for(p=299-m|0;Jt(a+912|0,120,I);){i=a+912|0;r:{if(l[a+912|0]!=35){s:if(!((0|(i=HA(a+912|0)-1|0))<=0))for(;;){if(!((0|(o=g[0|(f=(a+912|0)+i|0)]))==32|o-9>>>0<5))break s;if(g[0|f]=0,!((0|(i=i-1|0))>0))break}if(!(i=Hr(a+912|0)))break r}g[0|i]=0}i=a+912|0;r:if(o=l[a+912|0])for(;;){if(Or(o<<24>>24))break r;if(!(o=l[0|(i=i+1|0)]))break}if(g[0|i]=0,l[a+912|0]){i=i+1|0;r:switch(Cs(131904,a+912|0)-1|0){case 0:for(;o=i,i=i+1|0,(0|(f=g[0|o]))==32|f-9>>>0<5;);ar(a+832|0,o,80);continue;case 1:if(g[a+672|0]=0,e[a+364>>2]=5,o=a+672|0,e[a+16>>2]=o,e[a+20>>2]=a+364,ut(i,86237,a+16|0),(o=HA(o)+2|0)>>>0>=p>>>0)continue;g[0|(i=(a+368|0)+m|0)]=e[a+364>>2],qA(i+1|0,a+672|0),c=c+1|0,m=o+m|0;continue A;case 2:if(e[a+52>>2]=a+360,e[a+48>>2]=a+752,ut(i,86237,a+48|0),!r)continue;e[a+32>>2]=E,fr(T,92042,a+32|0);continue;case 5:break r;default:continue}e[a+64>>2]=a+356,ut(i,87268,a- -64|0)}}break}g[(a+368|0)+m|0]=0,o=Cs(132112,a+752|0),c?(f=HA(E)+m|0,p=wt((c=x0(28+(HA(a+832|0)+f|0)|0,1))+24|0,a+368|0,i=m+1|0),e[c+4>>2]=p,i=qA(i+p|0,E),e[c>>2]=i,e[c+8>>2]=i,l[a+832|0]&&(e[c>>2]=qA(2+(f+p|0)|0,a+832|0)),i=e[a+360>>2],g[c+14|0]=0,g[c+12|0]=o,g[c+13|0]=i,g[c+15|0]=e[a+356>>2],ta(I),i=e[50303],e[50303]=i+1,e[201216+(i<<2)>>2]=c):ta(I)}}else _s(a+96|0,t,r);if(!(i=u0(s)))break}E0(e[s+8>>2]),xt(s)}J=a+1040|0}function $s(A,t){var r,a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0;J=r=J-352|0;e:if(A||(A=e[136284+(t<<4)>>2])){l[0|A]!=47&&(e[r+12>>2]=A,e[r+4>>2]=47,e[r+8>>2]=47,e[r>>2]=137584,DA(A=r+16|0,84114,r)),g[r+240|0]=0;A:{r:{if(a=wa(A,84577)){if((0|Nn(a,20))==-1)break A;if(i=Sn(a),s=Sn(a),c=Sn(a),!((0|i)!=65537|(0|s)!=e[50754])&(0|c)==s<<1)break r;ta(a),b[r+256>>1]=l[84864]|l[84865]<<8,A=l[84852]|l[84853]<<8|l[84854]<<16|l[84855]<<24,e[r+240>>2]=l[84848]|l[84849]<<8|l[84850]<<16|l[84851]<<24,e[r+244>>2]=A,A=l[84860]|l[84861]<<8|l[84862]<<16|l[84863]<<24,e[r+248>>2]=l[84856]|l[84857]<<8|l[84858]<<16|l[84859]<<24,e[r+252>>2]=A,J=i=J-16|0;s:{if((a=HA(A=r+240|0))>>>0>=6&&!xa(c=(A+a|0)-6|0,84274,6)){for(m=100;;){for(f=0,J=a=J-16|0,l[227196]||(g[227197]=aA(),g[227196]=1),I=+Ue(),Ne(o=I/1e3)<9223372036854776e3?(p=Ne(o)>=1?~~(o>0?Je(AA(23283064365386963e-26*o),4294967295):xA(23283064365386963e-26*(o-+(~~o>>>0>>>0))))>>>0:0,s=~~o>>>0):(p=-2147483648,s=0),e[a>>2]=s,e[a+4>>2]=p,o=1e3*(I-(+(It(s,p,1e3,0)>>>0)+4294967296*+(0|$)))*1e3,s=Ne(o)<2147483648?~~o:-2147483648,e[a+8>>2]=s,s=c+(a>>>4|0)^z(e[a+8>>2],65537);g[c+f|0]=65+(15&s|s<<1&32),s=s>>>5|0,(0|(f=f+1|0))!=6;);if(J=a+16|0,e[i>>2]=384,(0|(a=p0(A,194,i)))>=0)break s;if(m=m-1|0,e[56798]!=20||!m)break}wt(c,84274,6)}else e[56798]=28;a=-1}J=i+16|0,(0|a)<0||E0(a)}if(!(a=wa(A,84577))){A=ps(0,e[56798],A);break e}}if((0|(i=ss(A)))<0){ta(a),A=ps(0,0-i|0,A);break e}if((0|Nn(a,0))==-1){t=e[56798],ta(a),A=ps(0,t,A);break e}if(!(s=Ur(e[(c=136280+(t<<4)|0)>>2],i))){ta(a),A=48;break e}if((0|mn(s,i,a))!=(0|i)){t=e[56798],ta(a),l[r+240|0]&&Fn(r+240|0),xt(s),A=ps(0,t,A);break e}ta(a),l[r+240|0]&&Fn(r+240|0),e[136276+(t<<4)>>2]=(l[s+40|0]|l[s+41|0]<<8|l[s+42|0]<<16|l[s+43|0]<<24)/2,e[c>>2]=s,A=0;break e}t=e[56798],ta(a),A=ps(0,t,A)}else A=28;return J=r+352|0,A}function mi(A,t,r,a,s,i){var o,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0;for(g[0|r]=1,o=1&i,ve=1,Z=-1,ee=-1,p=1,i=t;;){Qe=he-2|0,ce=I,T=ee;e:{A:{for(;;){if(!(E=l[0|i])){ee=T,I=ce;break e}if(i=i+1|0,L=e[144464+(E<<2)>>2]){r:{if((0|(c=l[L+11|0]))!=1){if(16&l[L+6|0]|(0|c)!=2)break r;g[0|(I=r+p|0)]=Z,c=(0|Z)<4|(0|T)>(0|Z),!(2&l[L+4|0])|!o|(0|Z)>=0||(g[0|I]=1),I=c?ce:p,ee=c?T:Z,Z=-1,p=p+1|0;break A}if(!M[L+8>>1]){c=0,m=p;s:if((0|E)!=8)c=l[L+14|0],e[s>>2]&&c>>>0>=4||(T=(0|c)<(0|T)?T:c,Z=c);else{for(;;){if(E=c,m=m-1|0,e[s>>2]|(0|m)<=0||(0|(L=g[0|(f=r+m|0)]))>3)break s;if(c=E+1|0,!(L>>>0<2))break}if(g[0|f]=4,ce=(0|T)<4?m:ce,T=(0|T)<=4?4:T,m>>>0<2)break s;if(L=3&(m=~E+he|0),c=1,Qe-E>>>0>=3)for(E=-4&m,m=0;l[0|(f=r+c|0)]==4&&(g[0|f]=3),l[f+1|0]==4&&(g[f+1|0]=3),l[f+2|0]==4&&(g[f+2|0]=3),l[f+3|0]==4&&(g[f+3|0]=3),c=c+4|0,(0|E)!=(0|(m=m+4|0)););if(f=0,!L)break s;for(;l[0|(E=r+c|0)]==4&&(g[0|E]=3),c=c+1|0,(0|L)!=(0|(f=f+1|0)););}if(ve)continue;break e}}break}}ee=T,I=ce,(0|E)==20&&(g[r+p|0]=o&&(0|Z)<0?1:Z,p=p+1|0)}if(g[0|t]=E,he=p-1|0,t=t+1|0,ve=(0|p)<99)continue}break}g[r+p|0]=1,g[0|t]=0;e:if((0|(t=e[s>>2]))>0)ee=4,(0|t)>=(0|p)&&(e[s>>2]=he,t=he),g[t+r|0]=4,I=e[s>>2];else if((0|ee)==5&&(ee=4,!((0|p)<2))){if(i=1,T=1&(t=p-1|0),(0|p)!=2)for(ce=-2&t,m=0;;){f=4;A:{r:{s:switch(l[0|(t=(c=i)+r|0)]-4|0){case 1:break r;case 0:break s;default:break A}f=2&l[A+14|0]?1:3,c=I}g[0|t]=f,I=c}f=4;A:{r:{s:switch(l[0|(t=(c=i+1|0)+r|0)]-4|0){case 1:break r;case 0:break s;default:break A}f=2&l[A+14|0]?1:3,c=I}g[0|t]=f,I=c}if(i=i+2|0,(0|ce)==(0|(m=m+2|0)))break}if(T){f=4;A:{r:switch(l[0|(t=r+i|0)]-4|0){case 1:break A;case 0:break r;default:break e}f=2&l[A+14|0]?1:3,i=I}g[0|t]=f,I=i}}return e[s>>2]=I,e[a>>2]=p,ee}function Ur(A,t){var r,a,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0;if(!A)return Ct(t);if(t>>>0>=4294967232)return e[56798]=48,0;r=t>>>0<11?16:t+11&-8,o=-8&(a=e[4+(c=A-8|0)>>2]);e:if(3&a){f=o+c|0;A:if(o>>>0>=r>>>0){if((i=o-r|0)>>>0<16)break A;e[c+4>>2]=1&a|r|2,e[4+(s=c+r|0)>>2]=3|i,e[f+4>>2]=1|e[f+4>>2],Tr(s,i)}else if(e[57158]!=(0|f))if(e[57157]!=(0|f)){if(2&(i=e[f+4>>2])||(p=o+(-8&i)|0)>>>0<r>>>0)break e;E=p-r|0;r:if(i>>>0<=255){if(o=e[f+8>>2],s=i>>>3|0,(0|(i=e[f+12>>2]))==(0|o)){e[57152]=e[57152]&aa(-2,s);break r}e[o+12>>2]=i,e[i+8>>2]=o}else{if(I=e[f+24>>2],(0|(m=e[f+12>>2]))==(0|f))if((s=e[(o=f+20|0)>>2])||(s=e[(o=f+16|0)>>2])){for(;i=o,m=s,(s=e[(o=s+20|0)>>2])||(o=m+16|0,s=e[m+16>>2]););e[i>>2]=0}else m=0;else s=e[f+8>>2],e[s+12>>2]=m,e[m+8>>2]=s;if(I){i=e[f+28>>2];s:{if(e[(s=228912+(i<<2)|0)>>2]==(0|f)){if(e[s>>2]=m,m)break s;e[57153]=e[57153]&aa(-2,i);break r}if(e[(e[I+16>>2]==(0|f)?16:20)+I>>2]=m,!m)break r}e[m+24>>2]=I,(s=e[f+16>>2])&&(e[m+16>>2]=s,e[s+24>>2]=m),(s=e[f+20>>2])&&(e[m+20>>2]=s,e[s+24>>2]=m)}}E>>>0<=15?(e[c+4>>2]=1&a|p|2,e[4+(s=c+p|0)>>2]=1|e[s+4>>2]):(e[c+4>>2]=1&a|r|2,e[4+(i=c+r|0)>>2]=3|E,e[4+(s=c+p|0)>>2]=1|e[s+4>>2],Tr(i,E))}else{if((i=o+e[57154]|0)>>>0<r>>>0)break e;(s=i-r|0)>>>0>=16?(e[c+4>>2]=1&a|r|2,e[4+(o=c+r|0)>>2]=1|s,e[(i=i+c|0)>>2]=s,e[i+4>>2]=-2&e[i+4>>2]):(e[c+4>>2]=i|1&a|2,e[4+(s=i+c|0)>>2]=1|e[s+4>>2],s=0,o=0),e[57157]=o,e[57154]=s}else{if((o=o+e[57155]|0)>>>0<=r>>>0)break e;e[c+4>>2]=1&a|r|2,s=o-r|0,e[4+(i=c+r|0)>>2]=1|s,e[57155]=s,e[57158]=i}s=c}else{if(r>>>0<256||o>>>0>=r+4>>>0&&(s=c,o-r>>>0<=e[57272]<<1>>>0))break e;s=0}return s?s+8|0:(c=Ct(t))?(wt(c,A,t>>>0>(s=(3&(s=e[A-4>>2])?-4:-8)+(-8&s)|0)>>>0?s:t),xt(A),c):0}function ts(){var A,t,r=0,a=0;return A=za(12),e[A>>2]=22050,t=za(432),e[(a=t)+4>>2]=0,e[a+8>>2]=0,e[a>>2]=132304,e[a+32>>2]=0,e[a+12>>2]=0,e[a+16>>2]=0,e[a+20>>2]=0,e[a+24>>2]=0,jA(a+40|0,0,376),e[a+420>>2]=0,e[a+424>>2]=-1,g[a+416|0]=1,r=jA(za(408),0,408),e[a+28>>2]=r,g[r+8|0]=1,e[A+4>>2]=a,r=za(1096),e[r+8>>2]=22050,e[r+4>>2]=22050,e[r>>2]=132352,e[r+64>>2]=22050,e[r+56>>2]=0,e[r+60>>2]=0,e[r+32>>2]=0,e[r+36>>2]=0,e[r+24>>2]=22050,e[r+16>>2]=0,e[r+20>>2]=0,e[r+40>>2]=0,e[r+44>>2]=0,g[r+48|0]=0,e[r+128>>2]=0,e[r+132>>2]=0,b[r+96>>1]=0,e[r+72>>2]=22050,e[r+136>>2]=0,e[r+140>>2]=0,b[r+168>>1]=0,e[r+144>>2]=22050,e[r+200>>2]=0,e[r+204>>2]=0,e[r+208>>2]=0,e[r+212>>2]=0,e[r+216>>2]=22050,b[r+240>>1]=0,e[r+280>>2]=0,e[r+284>>2]=0,e[r+272>>2]=0,e[r+276>>2]=0,e[r+288>>2]=22050,b[r+312>>1]=0,e[r+344>>2]=0,e[r+348>>2]=0,e[r+352>>2]=0,e[r+356>>2]=0,e[r+360>>2]=22050,b[r+384>>1]=0,e[r+416>>2]=0,e[r+420>>2]=0,e[r+424>>2]=0,e[r+428>>2]=0,e[r+432>>2]=22050,b[r+456>>1]=0,e[r+488>>2]=0,e[r+492>>2]=0,e[r+496>>2]=0,e[r+500>>2]=0,e[r+504>>2]=22050,b[r+528>>1]=1,e[r+560>>2]=0,e[r+564>>2]=0,e[r+568>>2]=0,e[r+572>>2]=0,b[r+600>>1]=0,e[r+576>>2]=22050,e[r+640>>2]=0,e[r+644>>2]=0,e[r+632>>2]=0,e[r+636>>2]=0,b[r+680>>1]=0,e[r+656>>2]=22050,e[r+648>>2]=22050,e[r+720>>2]=0,e[r+724>>2]=0,e[r+712>>2]=0,e[r+716>>2]=0,b[r+752>>1]=0,e[r+728>>2]=22050,e[r+792>>2]=0,e[r+796>>2]=0,e[r+784>>2]=0,e[r+788>>2]=0,b[r+824>>1]=0,e[r+800>>2]=22050,e[r+864>>2]=0,e[r+868>>2]=0,e[r+856>>2]=0,e[r+860>>2]=0,e[r+872>>2]=22050,b[r+896>>1]=0,e[r+936>>2]=0,e[r+940>>2]=0,e[r+928>>2]=0,e[r+932>>2]=0,b[r+968>>1]=0,e[r+944>>2]=22050,e[r+1008>>2]=0,e[r+1012>>2]=0,e[r+1e3>>2]=0,e[r+1004>>2]=0,b[r+1040>>1]=0,e[r+1016>>2]=22050,e[r+1088>>2]=0,e[(a=r+1080|0)>>2]=0,e[a+4>>2]=0,e[(a=r+1072|0)>>2]=0,e[a+4>>2]=0,e[A+8>>2]=r,ct[e[e[r>>2]+4>>2]](r,t),A}function ds(A,t,r){var a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0;e:if(I=e[50759]){if(a=e[50980],s=z(a,80)+222176|0,(0|(f=(0|(s=(0|(s=(e[s+12>>2]+e[s>>2]|0)/(0|A)|0))<(0|(p=((0|z(e[50754],19))/40<<16)/(0|A)|0))?s:p))>=399?399:s))>=0&&(jA(t,0,4+(f<<2)|0),a=e[50980]),(0|a)>=0)for(E=e[50801];;){if(i=z(o,80)+222176|0,e[i+4>>2]&&(c=e[i>>2])){if(T=c+e[i+12>>2]|0,a=1+((c-e[i+8>>2]|0)/(0|A)|0)|0,(0|c)>(0|(a=z(s=(0|a)<=1?1:a,A))))for(;e[(m=(s<<2)+t|0)>>2]=e[m>>2]+z(e[i+4>>2],l[((c-a|0)/(e[i+8>>2]>>8)|0)+E|0]),s=s+1|0,(0|c)>(0|(a=A+a|0)););if(!((0|a)>=(0|T)))for(;e[(m=(s<<2)+t|0)>>2]=e[m>>2]+z(e[i+4>>2],l[((a-c|0)/(e[i+12>>2]>>8)|0)+E|0]),s=s+1|0,(0|T)>(0|(a=A+a|0)););}if(!((0|(o=o+1|0))<=e[50980]))break}if(s=1,!((0|(i=65536e3/(0|A)|0))<=0||(0|(a=z(e[55565],10)))<=0))for(i=(0|a)/(0|i)|0;e[(c=(s<<2)+t|0)>>2]=e[c>>2]+a,s=s+1|0,(0|(a=a-i|0))>0;);if((0|o)<=8)for(;s=203216+(a=o<<2)|0,i=z(o,80)+222176|0,c=e[i+4>>2]>>14,e[s>>2]=(0|z(z(c,c),5))/2,r?a=e[a+203264>>2]:(c=a+203264|0,a=e[i>>2]/(0|A)|0,e[c>>2]=a),(0|a)>=(0|p)&&(e[s>>2]=0),(0|(o=o+1|0))!=9;);if(s=0,(0|f)>=0)for(a=0;i=e[(o=(a<<2)+t|0)>>2]>>15,i=z(i,i)>>8,e[o>>2]=i,(0|s)<=524287999&&(e[o>>2]=z(i,l[344+((s>>19)+I|0)|0])>>13),s=A+s|0,o=(0|a)!=(0|f),a=a+1|0,o;);if(e[t+4>>2]=(0|z(e[t+4>>2],l[203300]?6:10))/8,1&r)for(A=e[50826],a=1;;){if(e[203312+(r=a<<2)>>2]=e[t+r>>2]-e[A+r>>2]>>3,(0|(r=a+1|0))==30)break e;e[203312+(r<<=2)>>2]=e[t+r>>2]-e[A+r>>2]>>3,a=a+2|0}}else f=1;return f}function ei(A,t,r,a){var s,i=0,o=0;J=s=J-176|0,g[0|A]=0,b[s+80>>1]=24320,e[s+104>>2]=0,e[s+108>>2]=0,g[82+(zr(r,i=s+80|2)+s|0)|0]=0;e:{A:{if(!a){if(a=s+80|1,e[s+12>>2]=a,kr(t,s+12|0,s+16|0,s+104|0,0,0)||(e[s+12>>2]=i,kr(t,s+12|0,s+16|0,s+104|0,0,0)||(g[s+81|0]=32,qt(t,i,s+16|0,60,0,0,0))),(r=l[s+16|0])&&(0|r)!=21)break A;r:{if(e[t+212>>2]!=25966){if(wn(85719,188772,189296),g[s+81|0]=95,e[s+12>>2]=a,kr(e[47193],s+12|0,s+16|0,s+104|0,0,0)||(e[s+12>>2]=i,kr(e[47193],s+12|0,s+16|0,s+104|0,0,0)),l[s+16|0])break r;ha(e[e[32972]+60>>2]),r=l[s+16|0]}if(255&r)break A;t=l[87124]|l[87125]<<8|l[87126]<<16|l[87127]<<24,r=l[87120]|l[87121]<<8|l[87122]<<16|l[87123]<<24,g[0|A]=r,g[A+1|0]=r>>>8,g[A+2|0]=r>>>16,g[A+3|0]=r>>>24,g[A+4|0]=t,g[A+5|0]=t>>>8,g[A+6|0]=t>>>16,g[A+7|0]=t>>>24,g[A+16|0]=l[87136],t=l[87132]|l[87133]<<8|l[87134]<<16|l[87135]<<24,r=l[87128]|l[87129]<<8|l[87130]<<16|l[87131]<<24,g[A+8|0]=r,g[A+9|0]=r>>>8,g[A+10|0]=r>>>16,g[A+11|0]=r>>>24,g[A+12|0]=t,g[A+13|0]=t>>>8,g[A+14|0]=t>>>16,g[A+15|0]=t>>>24;break e}a=s+16|0,i=s+104|0,J=r=J-112|0,(o=e[47193])?($t(o,a,i,-1,0),JA(i=a,a=r+48|0),t=e[t+212>>2],g[r+43|0]=t>>>24,g[0|(o=(i=r+43|0)+(t>>>0>16777215)|0)]=t>>>16,g[0|(o=o+!!(16711680&t)|0)]=t>>>8,g[0|(o=o+!!(65280&t)|0)]=t,g[o+!!(255&t)|0]=0,e[r+16>>2]=85719,e[r+24>>2]=i,e[r+20>>2]=a,DA(A,85662,r+16|0)):($t(t,a,i,-1,0),JA(a,t=r+48|0),e[r>>2]=t,DA(A,85451,r)),J=r+112|0,ha(e[e[32972]+60>>2]);break e}if(e[s+12>>2]=i,kr(t,s+12|0,s+16|0,s+104|0,0,0),!l[s+16|0])break e}$t(r=t,t=s+16|0,s+104|0,-1,0),JA(r=t,t=s+112|0),e[s>>2]=t,DA(A,85451,s)}return J=s+176|0,A}function qa(A,t){var r,a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0;if(J=r=J-112|0,e[r+72>>2]=-1,e[(a=r- -64|0)>>2]=-1,e[a+4>>2]=-1,e[r+56>>2]=-1,e[r+60>>2]=-1,e[r+48>>2]=-1,e[r+52>>2]=-1,e[r+40>>2]=-1,e[r+44>>2]=-1,e[r+32>>2]=-1,e[r+36>>2]=-1,e[r+24>>2]=-1,e[r+28>>2]=-1,e[r+16>>2]=-1,e[r+20>>2]=-1,(0|t)>0){for(i=e[r+72>>2],o=e[r+68>>2],c=e[r+64>>2],f=e[r+60>>2],m=e[r+56>>2],p=e[r+52>>2],I=e[r+48>>2],E=e[r+44>>2],T=e[r+40>>2],L=e[r+36>>2],Z=e[r+32>>2],ee=e[r+28>>2],ce=e[r+24>>2],he=e[r+20>>2],ve=e[r+16>>2];s=i,i=(0|(i=e[60+(a=134912+(Qe<<6)|0)>>2]))<0?s:i,s=o,o=(0|(o=e[a+56>>2]))<0?s:o,s=c,c=(0|(c=e[a+52>>2]))<0?s:c,s=f,f=(0|(f=e[a+48>>2]))<0?s:f,s=m,m=(0|(m=e[a+44>>2]))<0?s:m,s=p,p=(0|(p=e[a+40>>2]))<0?s:p,s=I,I=(0|(I=e[a+36>>2]))<0?s:I,s=E,E=(0|(E=e[a+32>>2]))<0?s:E,s=T,T=(0|(T=e[a+28>>2]))<0?s:T,s=L,L=(0|(L=e[a+24>>2]))<0?s:L,s=Z,Z=(0|(Z=e[a+20>>2]))<0?s:Z,s=ee,ee=(0|(ee=e[a+16>>2]))<0?s:ee,s=ce,ce=(0|(ce=e[a+12>>2]))<0?s:ce,s=he,he=(0|(he=e[a+8>>2]))<0?s:he,ve=(0|(a=e[a+4>>2]))<0?ve:a,(0|(Qe=Qe+1|0))!=(0|t););e[r+72>>2]=i,e[r+68>>2]=o,e[r+64>>2]=c,e[r+60>>2]=f,e[r+56>>2]=m,e[r+52>>2]=p,e[r+48>>2]=I,e[r+44>>2]=E,e[r+40>>2]=T,e[r+36>>2]=L,e[r+32>>2]=Z,e[r+28>>2]=ee,e[r+24>>2]=ce,e[r+20>>2]=he,e[r+16>>2]=ve}for(t=0;;){if((0|(a=e[(i=t<<2)+(r+16|0)>>2]))!=e[(i=i+134848|0)>>2]){g[r+80|0]=0;e:{A:switch(t-1|0){case 4:e[47201]=a-1;break e;case 5:e[47200]=a;break e;case 0:case 1:case 2:case 3:case 11:break A;default:break e}e[r+4>>2]=a,e[r>>2]=1,e[r+8>>2]=g[t+102812|0],DA(r+80|0,91942,r)}e[i>>2]=a,a=r+80|0,qA(e[A>>2]+189424|0,a),e[A>>2]=e[A>>2]+HA(a)}if((0|(t=t+1|0))==15)break}J=r+112|0}function xs(A){var t,r,a,s=0,i=0,o=0,c=0;for(s=17,e[A+328>>2]=17,e[A+224>>2]=0,e[A+216>>2]=1105,e[A+220>>2]=1072,e[A+600>>2]=1056,e[A+8180>>2]=105296,o=jA(A+344|0,0,256),g[A+393|0]=1,g[A+365|0]=1,g[A+360|0]=1,g[A+545|0]=1,g[A+529|0]=1,g[A+391|0]=1,g[A+379|0]=1,g[A+374|0]=1,g[A+489|0]=1,g[A+487|0]=1,g[A+398|0]=1,g[A+387|0]=1,g[A+388|0]=2,g[A+389|0]=1,g[A+390|0]=1,g[A+385|0]=2,g[A+383|0]=2,g[A+368|0]=1,g[A+369|0]=2,i=104224;g[0|(s=s+o|0)]=4|l[0|s],s=o+l[i+1|0]|0,g[0|s]=4|l[0|s],s=o+l[i+2|0]|0,g[0|s]=4|l[0|s],s=l[0|(i=i+3|0)],(0|i)!=104251;);g[A+386|0]=8|l[A+386|0],g[A+382|0]=8|l[A+382|0],g[A+384|0]=8|l[A+384|0],g[A+369|0]=16|l[A+369|0],g[A+370|0]=16|l[A+370|0],g[A+371|0]=16|l[A+371|0],i=l[A+361|0],o=l[A+362|0],s=l[A+363|0],c=l[A+364|0],t=l[A+366|0],r=l[A+367|0],g[A+372|0]=16|l[A+372|0],g[A+373|0]=16|l[A+373|0],g[A+375|0]=16|l[A+375|0],g[A+376|0]=16|l[A+376|0],g[A+377|0]=16|l[A+377|0],g[A+378|0]=16|l[A+378|0],g[A+380|0]=16|l[A+380|0],g[A+381|0]=16|l[A+381|0],g[A+383|0]=16|l[A+383|0],g[A+385|0]=16|l[A+385|0],a=l[A+388|0],g[A+367|0]=48|r,g[A+366|0]=40|t,g[A+364|0]=48|c,g[A+363|0]=48|s,g[A+362|0]=48|o,g[A+361|0]=48|i,g[A+388|0]=80|a,i=l[A+390|0],o=l[A+391|0],s=l[A+393|0],g[A+360|0]=128|l[A+360|0],c=l[A+365|0],g[A+393|0]=192|s,g[A+365|0]=128|c,g[A+368|0]=128|l[A+368|0],g[A+374|0]=128|l[A+374|0],g[A+379|0]=128|l[A+379|0],g[A+387|0]=128|l[A+387|0],s=l[A+389|0],g[A+391|0]=192|o,g[A+390|0]=192|i,g[A+389|0]=128|s,g[A+529|0]=128|l[A+529|0],g[A+545|0]=128|l[A+545|0],g[A+489|0]=128|l[A+489|0],g[A+487|0]=128|l[A+487|0],g[A+398|0]=128|l[A+398|0]}function kr(A,t,r,a,s,i){var o,c=0,f=0,m=0,p=0,I=0,E=0;J=o=J-192|0,c=I=e[t>>2];e:{A:{for(;f=1,(0|(m=g[0|c]))>=0||(f=2,m>>>0<4294967264||(f=m>>>0<4294967280?3:4)),!(l[0|(m=f+c|0)]!=32|l[m+1|0]!=46);){if(p-160>>>0<4294967135)break A;wt((E=o+32|0)+p|0,c,f),g[(f=f+p|0)+E|0]=46,c=m+3|0,p=f+1|0}if(p){for(f=0;m=f,f=f+1|0,223&l[c+m|0];);if(!(1+(E=m+p|0)>>>0>160)&&(wt((f=o+32|0)+p|0,c,m),g[f+E|0]=0,ws(A,f,c,r,a,s,i))){e[a>>2]=128|e[a>>2],e[33264]=p,A=1;break e}}}for(c=0;;){if(I=(f=I)+1|0,223&(f=l[0|f]))if(!c|(0|f)!=46|g[31+(c+o|0)|0]-48>>>0>=10){if(g[(o+32|0)+c|0]=f,f=159,(0|(c=c+1|0))!=159)continue}else f=c;else f=c;break}g[(c=o+32|0)+f|0]=0,c=ws(A,c,I,r,a,s,i);A:if(8&l[a+3|0]){if(!vr(r,m=A+268|0)){if(m=e[A+288>>2]+1|0,e[A+288>>2]=m,(0|m)<4)break A;g[0|r]=0;break A}ar(m,r,20),e[A+288>>2]=1}else e[A+288>>2]=0;A:{if(!c){if(c=0,8&l[a+5|0]&&(m=Gt(o+28|0,c=o+32|l[o+32|0]==95),tA(A,e[o+28>>2],r),c=c+m|0),!(f>>>0<2|c)){if(g[0|r]=0,!(16&s&&l[0|(c=31+(f+o|0)|0)]==101)&&(!(4096&s)||l[0|(c=(f=(o+32|0)+f|0)-1|0)]!=l[f-2|0]))break A;g[0|c]=0,c=ws(A,o+32|0,I,r,a,s,i)}if(!c)break A}if(p=e[a>>2],l[A+172|0]&&(p^=536870912,e[a>>2]=p),A=1,!(536870912&p))break e;2&s&&(b[66448]=8192,e[o+16>>2]=r,DA(132898,87470,o+16|0),A=e[t>>2],e[t>>2]=132898,8&l[188788]&&(wt(t=o+32|0,a=A,A=c-A|0),g[A+t|0]=0,e[o+4>>2]=132898,A=e[47195],e[o>>2]=t,fr(A,87652,o)))}g[0|r]=0,A=0}return J=o+192|0,A}function Ss(A,t){var r=0,a=0,s=0;r=31&A;e:{A:{r:{if((0|(A&=96))==96)A=-1;else{if((0|A)!=64)break r;A=1}if(r>>>0>=15)break e;t=e[203136+(r<<2)>>2]+z(A,t)|0;break A}if(r>>>0>=15)break e}A=e[105616+(a=r<<2)>>2],e[a+203136>>2]=(0|t)>=0?(0|A)>(0|t)?t:A:0}e:{A:{r:{s:{i:switch(r-1|0){case 5:if(!(A=e[50759]))break A;e[54728]=e[50982],t=e[50979],r=e[50978],jA(205184,0,11e3),e[51293]=0,t=(r=(s=(0|(a=e[50789]))>0)?130:(0|r)>=5499?5499:r)?s?a:(0|t)>=100?100:t:0,e[50755]=t,r=(0|z(r,e[50754]))/1e3|0,e[51292]=r,e[54729]=(0|t)>20?r<<1:t?r:0,e[33037]=(0|z(500-t|0,(0|z(l[e[50797]+105596|0],(0|z(e[50787],55))/100|0))/16|0))/500;break s;case 0:break i;case 2:case 12:break e;case 4:break r;default:break A}if(!(A=e[50759]))break A}return t=256,(0|(r=(0|(r=e[50785]))>=101?101:r))>=51&&(t=256+(((z(r,25)-1250&65535)>>>0)/50|0)|0),b[A+164>>1]=(0|z(b[A+236>>1],t))/256,b[A+166>>1]=(0|z(b[A+238>>1],t))/256,b[A+168>>1]=(0|z(b[A+240>>1],t))/256,b[A+170>>1]=(0|z(b[A+242>>1],t))/256,b[A+172>>1]=(0|z(b[A+244>>1],t))/256,b[A+174>>1]=(0|z(b[A+246>>1],t))/256,A=e[50790],b[102e3]=(0|z(b[102036],z(A,-3)+256|0))/256,void(b[101999]=(0|z(b[102035],z(A,-6)+256|0))/256)}e[50759]&&(e[54728]=e[50982],A=e[50979],t=e[50978],jA(205184,0,11e3),e[51293]=0,A=(t=(a=(0|(r=e[50789]))>0)?130:(0|t)>=5499?5499:t)?a?r:(0|A)>=100?100:A:0,e[50755]=A,t=(0|z(t,e[50754]))/1e3|0,e[51292]=t,e[54729]=(0|A)>20?t<<1:A?t:0,e[33037]=(0|z(500-A|0,(0|z(l[e[50797]+105596|0],(0|z(e[50787],55))/100|0))/16|0))/500)}return}e[33037]=(0|z(l[e[50797]+105596|0],(0|z(e[50787],55))/100|0))/16}function Ns(A,t,r){var a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0;r&&(e[r>>2]=0);e:{A:if(!((0|(a=g[0|A]))<0)){for(;;){if((0|(i=255&a))==32|i-9>>>0<5){if((0|(a=g[0|(A=A+1|0)]))>=0)continue;break A}break}if(!(255&a))break e}for(;;){if((0|(a=f=255&a))==32|a-9>>>0<5)break e;if((0|f)!=124||(0|(a=l[0|(i=A+1|0)]))==124){A:{if((0|(I=e[36115]))>=2){for(a=1,i=-1,m=0;;){r:if(!(!(p=e[144464+(a<<2)>>2])|l[p+11|0]==15)){c=e[p>>2];s:{i:{if(f>>>0>=33){if(E=0,o=0,(255&c)==(0|f)&&(o=1,(s=l[A+1|0])>>>0<33|(0|s)!=(c>>>8&255)||(o=2,(s=l[A+2|0])>>>0<33|(0|s)!=(c>>>16&255)||(o=(s=(s=l[A+3|0])>>>0>32&(0|s)==(c>>>24|0))?4:3,E=0-s|0))),(0|i)>=(0|o))break r;if(s=4,!(1&E))break i;break s}if(o=0,(0|i)>=0)break r}if(c>>>((s=o)<<3)&255)break r}m=l[p+10|0],i=s}if((0|I)==(0|(a=a+1|0)))break}if(m)break A}return r&&Gt(r,A),void(g[0|t]=0)}g[0|t]=m,A=((0|i)<=1?1:i)+A|0,t=i=t+1|0;A:if((0|m)==21){r:if((0|(s=l[0|A]))==32|s-9>>>0<5)a=i;else if(a=i,s)for(;;){if(g[0|a]=Ht(s),a=a+1|0,(0|(s=l[0|(A=A+1|0)]))==32|s-9>>>0<5)break r;if(!s)break}if(g[0|a]=0,!s){if(t=a,vr(i,85593))break A;return void(g[0|i]=0)}g[0|a]=124,t=a+1|0}a=l[0|A]}else A=i;if(!(255&a))break}}g[0|t]=0}function Ci(A,t){var r=0,a=0,s=0,i=0,o=0;e:{A:{r:{s:{i:switch((0|(r=e[A+4>>2]))==e[A+104>>2]?r=bA(A):(e[A+4>>2]=r+1,r=l[0|r]),r-43|0){case 0:case 2:break i;default:break s}if(i=(0|r)==45,o=!t,(0|(r=e[A+4>>2]))==e[A+104>>2]?r=bA(A):(e[A+4>>2]=r+1,r=l[0|r]),o|(t=r-58|0)>>>0>4294967285)break r;if(e[A+116>>2]<0)break A;e[A+4>>2]=e[A+4>>2]-1;break A}t=r-58|0}if(!(t>>>0<4294967286)){if((t=r-48|0)>>>0<10){for(;s=(0|(a=(a=z(a,10)+r|0)-48|0))<214748364,(0|(t=e[A+4>>2]))==e[A+104>>2]?r=bA(A):(e[A+4>>2]=t+1,r=l[0|t]),s&(t=r-48|0)>>>0<=9;);s=a>>31}r:if(!(t>>>0>=10))for(;;){if(t=(a=It(a,s,10,0))+r|0,r=$,r=t>>>0<a>>>0?r+1|0:r,a=t-48|0,s=r-(t>>>0<48)|0,(0|(t=e[A+4>>2]))==e[A+104>>2]?r=bA(A):(e[A+4>>2]=t+1,r=l[0|t]),(t=r-48|0)>>>0>9)break r;if(!(a>>>0<2061584302&(0|s)<=21474836|(0|s)<21474836))break}if(t>>>0<10)for(;(0|(t=e[A+4>>2]))==e[A+104>>2]?t=bA(A):(e[A+4>>2]=t+1,t=l[0|t]),t-48>>>0<10;);(0|(t=e[A+116>>2]))>0|(0|t)>=0&&(e[A+4>>2]=e[A+4>>2]-1),A=a,a=i?0-A|0:A,s=i?0-(!!(0|A)+s|0)|0:s;break e}}if(s=-2147483648,!(e[A+116>>2]<0))return e[A+4>>2]=e[A+4>>2]-1,$=-2147483648,0}return $=s,a}function Os(A){var t=0,r=0,a=0,s=0;if(e[36432]=110,e[36433]=100,e[36434]=450,e[36430]=5,t=e[203136+((0|A)==2?32:8)>>2],a=e[32972],(0|(r=e[a+84>>2]))>0&&(t=(0|z(t,r))/100|0),r=(0|t)>=359?359:t,r=(0|(t=(0|t)>=450?450:t))>399?6:(0|t)>379?7:l[((0|r)<=80?80:r)+101856|0],1&A&&(e[32526]=(0|z(r,e[a+72>>2]))/256,e[32527]=(0|z(r,e[a+76>>2]))/256,e[32528]=(0|z(r,e[a+80>>2]))/256,r>>>0>7||(s=r-1|0,e[32528]=s,e[32526]=r,e[32527]=s)),2&A){A=e[a+72>>2];e:{A:{r:{s:{i:{c:{o:{u:{if((0|t)>=351)a=t-350|0,e[36432]=85-(((255&a)>>>0)/3|0)&255,a=60-(a>>>3|0)|0;else{if((0|t)<251)break u;a=t-250|0,e[36432]=110-(a>>>2|0),a=110-(a>>>1|0)|0}if(e[36433]=a,A=(0|z(A,r))/256|0,e[36431]=110+((0|z(A,150))/128|0),t>>>0<=349)break o;if(r=t-350|0,e[36431]=l[r+102224|0],t>>>0<390)break i;if(e[36434]=450+((t+112<<24>>24)/-2<<24>>24),t>>>0<441)break c;e[36434]=860-t,A=12;break A}A=(0|z(A,r))/256|0,e[36431]=(0|t)>=170?110+((0|z(A,150))/128|0)|0:128+((A<<7)/130|0)|0}A=(A<<8)/115|0;break A}if(A=12,t>>>0>430)break A;if(A=13,t>>>0<=400)break s;break A}if(A=(A<<8)/115|0,e[36428]=A,t>>>0<375)break r}A=14;break A}if((0|t)<351)break e;A=l[r+102336|0]}e[36428]=A}e[36429]=(0|A)<=16?16:A}}function Bs(A,t,r){var a,s,i,o;a=.000244140625*+e[50767],D[r>>3]=a,D[r+40>>3]=.015625*+e[A+112>>2],D[r+48>>3]=.015625*+e[A+276>>2],D[r+56>>3]=.00390625*+(0|z(b[A+166>>1],b[t+4>>1]))+ +b[A+220>>1],D[r+64>>3]=.00390625*+(0|z(b[A+168>>1],b[t+6>>1]))+ +b[A+222>>1],D[r+72>>3]=.00390625*+(0|z(b[A+170>>1],b[t+8>>1]))+ +b[A+224>>1],D[r+80>>3]=.00390625*+(0|z(b[A+172>>1],b[t+10>>1]))+ +b[A+226>>1],D[r+88>>3]=.00390625*+(0|z(b[A+174>>1],b[t+12>>1]))+ +b[A+228>>1],s=b[A+230>>1],i=b[A+176>>1],o=b[t+14>>1],e[r+112>>2]=0,e[r+116>>2]=1080623104,e[r+104>>2]=0,e[r+108>>2]=1081032704,D[r+96>>3]=.00390625*+(0|z(i,o))+ +(0|s),l[t+40|0]?(e[r+184>>2]=0,e[r+188>>2]=1072693248,D[r+104>>3]=l[t+40|0]<<1):(e[r+184>>2]=0,e[r+188>>2]=0),D[r+120>>3]=.00390625*+b[A+202>>1]*+(l[t+35|0]<<1),D[r+128>>3]=.00390625*+b[A+204>>1]*+(l[t+36|0]<<1),D[r+136>>3]=.00390625*+b[A+206>>1]*+(l[t+37|0]<<1),t=l[t+38|0],A=b[A+208>>1],e[r+176>>2]=0,e[r+180>>2]=1079574528,e[r+160>>2]=0,e[r+164>>2]=1083129856,e[r+152>>2]=0,e[r+156>>2]=1083129856,e[r+352>>2]=0,e[r+356>>2]=1072693248,e[r+168>>2]=0,e[r+172>>2]=1079574528,D[r+144>>3]=.00390625*+(0|A)*+(t<<1),A=e[50779],D[r+368>>3]=a,D[r+360>>3]=+(0|A)/100*3}function Ls(A){var t=0;Se(A,Wa(A));e:{A:{r:{s:{i:{c:{o:{u:{l:{n:{if((0|(A=-1048576&$))<268435455|(0|A)<=268435455){p:{C:{if((0|A)<33554431|(0|A)<=33554431){if((0|A)<8388607|(0|A)<=8388607){if(t=524328,!0&(0|A)==-2147483648)break e;if(0|(0|A)!=-2143289344)break A;return 557096}if(!0&(0|A)==8388608)break C;if(0|(0|A)!=16777216)break A;return 524358}if((0|A)>71303167)break p;if(!0&(0|A)==33554432)break r;if(0|(0|A)!=67108864)break A}return 266270}if(!0&(0|A)==71303168)break n;if(!0&(0|A)==134217728)break s;if(0|(0|A)!=138412032)break A;return 294942}if((0|A)<542113791|(0|A)<=542113791){if((0|A)<536870911|(0|A)<=536870911){if(!0&(0|A)==268435456)break i;if(0|(0|A)!=272629760)break A;return 299028}if(!0&(0|A)==536870912)break o;if(!0&(0|A)==538968064)break c;if(0|(0|A)!=541065216)break A;return 569389}if((0|A)<1075838975|(0|A)<=1075838975){if(!0&(0|A)==542113792)break n;if(0|(0|A)!=1073741824)break A;return 532520}if(!0&(0|A)==1075838976)break u;if(!0&(0|A)==1077936128)break l;if(0|(0|A)!=1078984704)break A}return 299038}return 565288}return 1581096}return 536621}return 1585197}return 266260}return 262174}return 2396190}t=16384}return t}function vs(A,t,r,a,s,i){var o,c,f=0,m=0,p=0,I=0;if(c=8388607&A,m=e[34456],f=l[0|(A=c+m|0)]|l[A+1|0]<<8){o=!(p=l[A+2|0]),A=e[36434]<<o,(0|r)<=0?r=f:(r=(0|z(e[50754],r))/1e3<<o,A=(0|A)<(0|(I=(0|z(r,A))/(0|f)|0))?I:A),s=(0|s)>0?(0|z(r,s))/256|0:r,r=(0|z(s,e[36431]))/256|0,r=(0|A)<(0|(r=(4&a)>>>2|0&&(0|r)>(0|s)?s:r))?r:A,p||(f=f>>>1|0,r=(0|r)/2|0);e:if(!((0|i)<0)){if(a=c+4|0,256&t)A=e[50758],e[36439]=A,e[(t=216192+(A<<4)|0)>>2]=7,e[t+8>>2]=a+m,e[t+4>>2]=f<<16|r,i=p|i<<8;else{if(A=e[50758],e[36439]=A,e[(A=216192+(A<<4)|0)>>2]=6,i=p|i<<8,e[A+12>>2]=i,e[A+8>>2]=a+m,m=A,A=z(t=f>>>2|0,3),s=(0|r)>(0|f),e[m+4>>2]=s?A:r,m=e[50758]+1|0,e[50758]=(0|m)<=169?m:0,(0|A)<(0|(r=s?r-A|0:0)))for(s=t<<1,p=a+(p?t:s)|0;t=e[50758],e[36439]=t,e[(t=216192+(t<<4)|0)>>2]=6,e[t+4>>2]=s,e[t+12>>2]=i,e[t+8>>2]=p+e[34456],t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,(0|A)<(0|(r=r-s|0)););if((0|r)<=0)break e;A=e[50758],e[36439]=A,e[(t=216192+(A<<4)|0)>>2]=6,e[t+4>>2]=r,e[t+8>>2]=e[34456]+(a+(f-r<<o)|0)}e[12+(216192+(A<<4)|0)>>2]=i,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0}}}function wt(A,t,r){var a,s=0,i=0;if(r>>>0>=512)return iA(0|A,0|t,0|r),A;a=A+r|0;e:if(3&(A^t))if(a>>>0<4)r=A;else if((s=a-4|0)>>>0<A>>>0)r=A;else for(r=A;g[0|r]=l[0|t],g[r+1|0]=l[t+1|0],g[r+2|0]=l[t+2|0],g[r+3|0]=l[t+3|0],t=t+4|0,s>>>0>=(r=r+4|0)>>>0;);else{A:if(3&A)if(r)for(r=A;;){if(g[0|r]=l[0|t],t=t+1|0,!(3&(r=r+1|0)))break A;if(!(r>>>0<a>>>0))break}else r=A;else r=A;if(!((s=-4&a)>>>0<64||(i=s+-64|0)>>>0<r>>>0))for(;e[r>>2]=e[t>>2],e[r+4>>2]=e[t+4>>2],e[r+8>>2]=e[t+8>>2],e[r+12>>2]=e[t+12>>2],e[r+16>>2]=e[t+16>>2],e[r+20>>2]=e[t+20>>2],e[r+24>>2]=e[t+24>>2],e[r+28>>2]=e[t+28>>2],e[r+32>>2]=e[t+32>>2],e[r+36>>2]=e[t+36>>2],e[r+40>>2]=e[t+40>>2],e[r+44>>2]=e[t+44>>2],e[r+48>>2]=e[t+48>>2],e[r+52>>2]=e[t+52>>2],e[r+56>>2]=e[t+56>>2],e[r+60>>2]=e[t+60>>2],t=t- -64|0,i>>>0>=(r=r- -64|0)>>>0;);if(r>>>0>=s>>>0)break e;for(;e[r>>2]=e[t>>2],t=t+4|0,s>>>0>(r=r+4|0)>>>0;);}if(r>>>0<a>>>0)for(;g[0|r]=l[0|t],t=t+1|0,(0|a)!=(0|(r=r+1|0)););return A}function Pt(A){var t=0,r=0,a=0,s=0,i=0,o=0,c=0,f=0;be(+A),t=0|X(1),X(0);e:{if((a=(t=t>>>20&2047)-969|0)>>>0<63)f=t;else{if((0|a)<0)return A+1;if(!(t>>>0<1033)){if(be(+A),a=0|X(1),r=0,!(0|X(0))&(0|a)==-1048576)break e;return t>>>0>=2047?A+1:(0|a)<0?(D[8+(t=J-16|0)>>3]=12882297539194267e-247,12882297539194267e-247*D[t+8>>3]):(D[8+(t=J-16|0)>>3]=3105036184601418e216,3105036184601418e216*D[t+8>>3])}}if(r=D[14409],i=(r=(A=(r=(s=D[14408]*A+r)-r)*D[14411]+(r*D[14410]+A))*A)*r*(A*D[14415]+D[14414]),r*=A*D[14413]+D[14412],be(+s),X(1),c=0|X(0),A=i+(r+(D[115376+(a=c<<4&2032)>>3]+A)),o=e[(a=a+115384|0)>>2],t=(c<<13)+(t=e[a+4>>2])|0,t=(a=(a=o)+(o=0)|0)>>>0<o>>>0?t+1|0:t,!f)return-2147483648&c?(H(0,0|a),H(1,t+1071644672|0),(A=(s=(r=+re())*A)+r)<1&&(e[8+(t=J-16|0)>>2]=0,e[t+12>>2]=1048576,D[t+8>>3]=22250738585072014e-324*D[t+8>>3],A=(A=(i=A+1)+(s+(r-A)+(A+(1-i)))+-1)==0?0:A),A*=22250738585072014e-324):(H(0,0|a),H(1,t-1058013184|0),A=5486124068793689e288*((r=+re())*A+r)),A;H(0,0|a),H(1,0|t),r=(r=+re())*A+r}return r}function wa(A,t){var r,a=0,s=0,i=0,o=0;J=r=J-16|0;e:{if($a(84270,g[0|t])){if(s=2,$a(t,43)||(s=l[0|t]!=114),s=$a(t,120)?128|s:s,i=s=$a(t,101)?524288|s:s,o=64|s,i=(0|(s=l[0|t]))==114?i:o,i=(0|s)==119?512|i:i,e[r>>2]=438,e[r+4>>2]=0,(A=0|ze(-100,0|A,32768|((0|s)==97?1024|i:i),0|r))>>>0>=4294963201&&(e[56798]=0-A,A=-1),(0|A)<0)break e;J=s=J-32|0;A:{r:{if($a(84270,g[0|t])){if(a=Ct(1176))break r}else e[56798]=28;t=0;break A}jA(a,0,144),$a(t,43)||(e[a>>2]=l[0|t]==114?8:4),l[0|t]==97?(1024&(t=0|Ie(0|A,3,0))||(t|=1024,e[s+16>>2]=t,e[s+20>>2]=t>>31,Ie(0|A,4,s+16|0)),t=128|e[a>>2],e[a>>2]=t):t=e[a>>2],e[a+80>>2]=-1,e[a+48>>2]=1024,e[a+60>>2]=A,e[a+44>>2]=a+152,8&t||(e[s>>2]=s+24,e[s+4>>2]=0,0|kA(0|A,21523,0|s)||(e[a+80>>2]=10)),e[a+40>>2]=10,e[a+36>>2]=11,e[a+32>>2]=12,e[a+12>>2]=13,l[227205]||(e[a+76>>2]=-1),e[a+56>>2]=e[56816],(t=e[56816])&&(e[t+52>>2]=a),e[56816]=a,t=a}if(J=s+32|0,a=t)break e;GA(0|A)}else e[56798]=28;a=0}return J=r+16|0,a}function Ai(A,t,r){var a,s=0,i=0,o=0,c=0,f=0;if(o=A,J=a=J-208|0,e[a+8>>2]=1,e[a+12>>2]=0,c=t<<2){for(e[a+16>>2]=4,e[a+20>>2]=4,t=4,s=4,i=2;A=t,t=(s+4|0)+t|0,e[(a+16|0)+(i<<2)>>2]=t,i=i+1|0,s=A,t>>>0<c>>>0;);if((A=(o+c|0)-4|0)>>>0<=o>>>0)i=0,t=1,A=0;else{for(i=1,t=1;3&~i?(ue[(a+16|0)+((s=t-1|0)<<2)>>2]>=A-o>>>0?Pr(o,r,a+8|0,t,0,a+16|0):In(o,r,t,a+16|0),(0|t)!=1?(Ki(a+8|0,s),t=1):(Ki(a+8|0,1),t=0)):(In(o,r,t,a+16|0),Bi(a+8|0,2),t=t+2|0),i=1|(s=e[a+8>>2]),e[a+8>>2]=i,A>>>0>(o=o+4|0)>>>0;);i=s>>>0>1,A=e[a+12>>2]!=0}if(Pr(o,r,a+8|0,t,0,a+16|0),i|(0|t)!=1|A)for(;(0|t)<=1?(Bi(s=a+8|0,A=Dn(s)),i=e[a+8>>2],A=A+t|0):(Ki(s=a+8|0,2),e[a+8>>2]=7^e[a+8>>2],Bi(s,1),Pr((f=o-4|0)-e[(c=a+16|0)+((A=t-2|0)<<2)>>2]|0,r,s,t-1|0,1,c),Ki(s,1),i=1|e[a+8>>2],e[a+8>>2]=i,Pr(f,r,s,A,1,c)),t=A,o=o-4|0,e[a+12>>2]|(0|t)!=1|(0|i)!=1;);}J=a+208|0}function ys(A,t,r,a){var s,i=0,o=0,c=0;J=s=J-32|0,c=i=2147483647&a,o=i-1006698496|0;e:if(0|(i=i-1140785152|0)>>>0>o>>>0){if(i=r<<4|t>>>28,r=a<<4|r>>>28,(0|(t&=268435455))==134217728&!!(0|A)|t>>>0>134217728){o=r+1073741824|0,o=(i=i+1|0)?o:o+1|0;break e}if(o=r+1073741824|0,A|(0|t)!=134217728)break e;o=(A=1&i)>>>0>(i=A+i|0)>>>0?o+1|0:o}else(!r&(0|c)==2147418112?!(A|t):c>>>0<2147418112)?(i=0,o=2146435072,c>>>0>1140785151||(o=0,(c=c>>>16|0)>>>0<15249||(ea(s+16|0,A,t,r,i=65535&a|65536,c-15233|0),Ya(s,A,t,r,i,15361-c|0),i=(t=e[s+8>>2])<<4,t=e[s+12>>2]<<4|t>>>28,r=e[s>>2],c=o=e[s+4>>2],i|=o>>>28,o=t,(0|(A=268435455&c))==134217728&!!(0|(t=r|!!(e[s+16>>2]|e[s+24>>2]|e[s+20>>2]|e[s+28>>2])))|A>>>0>134217728?o=(i=i+1|0)?o:o+1|0:t|(0|A)!=134217728||(o=(A=i)>>>0>(i=i+(1&i)|0)>>>0?o+1|0:o)))):(i=r<<4|t>>>28,o=524287&(A=a<<4|r>>>28)|2146959360);return J=s+32|0,H(0,0|i),H(1,-2147483648&a|o),+re()}function Ys(A){var t,r=0,a=0,s=0,i=0,o=0;if(be(+A),o=0|X(1),s=0|X(0),(0|(i=o>>>20&2047))==2047)return(A*=1)/A;if(!(a=s<<1)&(0|(r=o<<1|s>>>31))==2145386496|r>>>0<2145386496)return!a&(0|r)==2145386496?0*A:A;if(i)r=1048575&o|1048576;else{if(i=0,a=s<<12,(0|(r=o<<12|s>>>20))>0|(0|r)>=0)for(;i=i-1|0,r=r<<1|a>>>31,a<<=1,(0|r)>0|(0|r)>=0;);a=31&(r=1-i|0),(63&r)>>>0>=32?(r=s<<a,s=0):(r=(1<<a)-1&s>>>32-a|o<<a,s<<=a)}if(a=s,(0|i)>1023){for(;;){if(!((0|(s=r+-1048576|0))<0||(r=s)|a))return 0*A;if(r=r<<1|a>>>31,a<<=1,!((0|(i=i-1|0))>1023))break}i=1023}if(!((0|(s=r+-1048576|0))<0||(r=s)|a))return 0*A;if((0|r)==1048575|r>>>0<1048575)for(;i=i-1|0,s=r>>>0<524288,r=r<<1|a>>>31,a<<=1,s;);return t=-2147483648&o,(0|i)>0?r=r+-1048576|i<<20:(s=1-i|0,o=r,i=a,a=31&s,(63&s)>>>0>=32?(r=0,a=o>>>a|0):(r=o>>>a|0,a=((1<<a)-1&o)<<32-a|i>>>a)),H(0,0|a),H(1,r|t),+re()}function rs(A,t,r,a,s){var i,o=0,c=0;J=i=J-160|0;e:{A:{r:{s:switch((o=l[t+10|0])-15|0){case 6:break r;case 0:break s;default:break A}g[0|A]=0;break e}e[i>>2]=z(l[r+7|0],44)+137856,DA(A,86002,i),A=HA(A)+A|0;break e}if(a){if(g[i+140|0]=0,r?tr(0,0,r,i+8|0,0):Hi(o,i+8|0),r=i+140|0,o=l[i+140|0]){if((0|o)==32){g[0|A]=0;break e}224&(o=o<<24>>24)||(s&&(e[s>>2]=o),r=i+141|0)}if(!((0|(s=HA(r)))<=0)){A=qA(A,r)+s|0,g[0|A]=0;break e}}s=0;A:if(!(!(r=255&(o=e[t>>2]))|(0|r)==47)){if(a){if((0|(c=255&o))==95)break A;r:{s:{if((0|c)==35){if(c=3,l[t+11|0]!=2)break s;break A}if((c=r-32|0)>>>0>95)break r}r=M[93952+(c<<1)>>1]}s=zr(r,A)}else g[0|A]=o,s=1;for(;;){if(!(r=255&(o>>=8))|(0|r)==47)break A;if(a){if((0|r)==35&l[t+11|0]==2)break A;if(r-48>>>0<10)continue;(c=r-32|0)>>>0<=95&&(r=M[93952+(c<<1)>>1]),s=zr(r,A+s|0)+s|0}else g[A+s|0]=o,s=s+1|0}}g[0|(A=A+s|0)]=0}return J=i+160|0,A}function Hs(A){var t,r=0;t=A,r=131280;e:{A:{if(!((0|A)<=1023||(r=131300,A>>>0<1328||(r=131320,A>>>0<1424||(r=131340,A>>>0<1536||(r=131360,A>>>0<1792||(r=131380,A>>>0<1872||(r=131400,A>>>0<2432||(r=131420,A>>>0<2560||(r=131440,A>>>0<2688||(r=131460,A>>>0<2816||(r=131480,A>>>0<2944||(r=131500,A>>>0<3072||(r=131520,A>>>0<3200||(r=131540,A>>>0<3328||(r=131560,A>>>0<3456||(r=131580,A>>>0<3584||(r=131600,A>>>0<3712||(r=131620,A>>>0<3840||(r=131640,A>>>0<4096||(r=131660,A>>>0<4256||(r=131680,A>>>0<4352||(r=131700,A>>>0<4608||(r=131720,A>>>0<5024||(r=131740,A>>>0<10496||(r=131760,A>>>0<12544||(r=131780,A>>>0<40960))))))))))))))))))))))))))){if(A>>>0>=55296)break A;r=131800}if((0|t)>=M[(A=r)+8>>1])break e}A=0}return A}function tA(A,t,r){var a,s=0,i=0,o=0,c=0;J=a=J-208|0,g[a+80|0]=0;e:{if((s=t-224|0)>>>0<=158)t=101072+(s<<1)|0;else{if((t=t-592|0)>>>0>88)break e;t=101392+(t<<1)|0}if(t=M[t>>1]){if(o=t<<16>>16,c=(s=63&t)>>>0>37?s+59|0:b[101584+(s<<1)>>1],s=t>>>6|0,(0|o)<0)s=59+(63&s)|0,t=t>>>12&7;else{if(!(i=31&s))break e;s=0,t=t>>>11&15}(i=RA(A,e[129920+(i<<3)>>2],a+112|0))&&b0(A,c,a+176|0)&&(t&&4096&RA(A,e[129920+(t<<3)>>2],a+80|0)&&(r=HA(t=qA(r,a+80|0)),g[a+80|0]=0,r=t+r|0),s?(b0(t=A,s,A=a+144|0),e[a+68>>2]=a+80,e[a- -64>>2]=A,e[a+60>>2]=6,e[a+52>>2]=23,e[a+56>>2]=a+176,e[a+48>>2]=a+112,DA(r,84101,a+48|0)):(0|o)<0?qA(r,a+176|0):1&e[A+144>>2]|4096&i?(e[a+36>>2]=23,e[a+40>>2]=6,e[a+44>>2]=a+176,e[a+32>>2]=a+112,DA(r,84430,a+32|0)):(e[a+16>>2]=23,e[a+8>>2]=23,e[a>>2]=4,e[a+12>>2]=a+112,e[a+4>>2]=a+176,DA(r,84802,a)))}}J=a+208|0}function _(){ji(),e[55928]=0,e[55926]=0,e[55927]=0,e[55924]=0,e[56244]=0,e[56245]=0,e[56246]=0,e[56247]=0,e[56260]=0,e[56261]=0,e[56262]=0,e[56263]=0,e[56276]=0,e[56277]=0,e[56278]=0,e[56279]=0,e[55974]=0,e[55975]=0,e[55972]=0,e[55973]=0,e[55988]=0,e[55989]=0,e[55990]=0,e[55991]=0,e[56004]=0,e[56005]=0,e[56006]=0,e[56007]=0,e[56020]=0,e[56021]=0,e[56022]=0,e[56023]=0,e[56036]=0,e[56037]=0,e[56038]=0,e[56039]=0,e[56052]=0,e[56053]=0,e[56054]=0,e[56055]=0,e[56068]=0,e[56069]=0,e[56070]=0,e[56071]=0,e[56086]=0,e[56087]=0,e[56084]=0,e[56085]=0,e[56102]=0,e[56103]=0,e[56100]=0,e[56101]=0,e[56118]=0,e[56119]=0,e[56116]=0,e[56117]=0,e[56134]=0,e[56135]=0,e[56132]=0,e[56133]=0,e[56150]=0,e[56151]=0,e[56148]=0,e[56149]=0,e[56166]=0,e[56167]=0,e[56164]=0,e[56165]=0,e[56182]=0,e[56183]=0,e[56180]=0,e[56181]=0,e[56198]=0,e[56199]=0,e[56196]=0,e[56197]=0,e[56214]=0,e[56215]=0,e[56212]=0,e[56213]=0,e[56230]=0,e[56231]=0,e[56228]=0,e[56229]=0}function Be(A,t){var r=0,a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0;e:{if((0|(i=e[A+4>>2]))==e[A>>2])if((o=e[A+8>>2])>>>0<(r=e[A+12>>2])>>>0)r=(s=(1+(r-o>>2)|0)/2<<2)+o|0,(0|i)!=(0|o)&&(Yt(r=r-(a=o-i|0)|0,i,a),i=e[A+8>>2]),e[A+4>>2]=r,e[A+8>>2]=s+i;else{if((a=(0|r)==(0|i)?1:r-i>>1)>>>0>=1073741824)break e;if(p=(f=za(r=a<<2))+r|0,c=r=(a+3&-4)+f|0,(0|i)!=(0|o)){if(I=-4&(o=o-i|0),s=r,a=i,o=1+((m=o-4|0)>>>2|0)&7)for(c=0;e[s>>2]=e[a>>2],a=a+4|0,s=s+4|0,(0|o)!=(0|(c=c+1|0)););if(c=r+I|0,!(m>>>0<28))for(;e[s>>2]=e[a>>2],e[s+4>>2]=e[a+4>>2],e[s+8>>2]=e[a+8>>2],e[s+12>>2]=e[a+12>>2],e[s+16>>2]=e[a+16>>2],e[s+20>>2]=e[a+20>>2],e[s+24>>2]=e[a+24>>2],e[s+28>>2]=e[a+28>>2],a=a+32|0,(0|c)!=(0|(s=s+32|0)););}e[A+12>>2]=p,e[A+8>>2]=c,e[A+4>>2]=r,e[A>>2]=f,i&&(xt(i),r=e[A+4>>2])}else r=i;return e[r-4>>2]=e[t>>2],void(e[A+4>>2]=e[A+4>>2]-4)}Zi(),xe()}function Oe(A,t,r){var a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0;e:{A:{r:{s:{i:{c:{o:{u:{l:{if(t){if(!r)break l;break u}return OA=(t=A)-z(A=(A>>>0)/(r>>>0)|0,r)|0,ge=0,$=0,A}if(!A)break o;break c}if(!((a=r-1|0)&r))break i;i=0-(o=(Ye(r)+33|0)-Ye(t)|0)|0;break r}return OA=0,ge=t-z(A=(t>>>0)/0|0,0)|0,$=0,A}if((a=32-Ye(t)|0)>>>0<31)break s;break A}if(OA=A&a,ge=0,(0|r)==1)break e;return r=31&(a=Ei(r)),(63&a)>>>0>=32?A=t>>>r|0:(s=t>>>r|0,A=((1<<r)-1&t)<<32-r|A>>>r),$=s,A}o=a+1|0,i=63-a|0}if(a=31&(s=63&o),s>>>0>=32?(s=0,c=t>>>a|0):(s=t>>>a|0,c=((1<<a)-1&t)<<32-a|A>>>a),a=31&(i&=63),i>>>0>=32?(t=A<<a,A=0):(t=(1<<a)-1&A>>>32-a|t<<a,A<<=a),o)for(p=(0|(a=r-1|0))==-1?-1:0;f=s<<1|c>>>31,c=(s=c<<1|t>>>31)-(m=r&(i=p-(f+(s>>>0>a>>>0)|0)>>31))|0,s=f-(s>>>0<m>>>0)|0,t=t<<1|A>>>31,A=I|A<<1,I=f=1&i,o=o-1|0;);return OA=c,ge=s,$=t<<1|A>>>31,f|A<<1}OA=A,ge=t,A=0,t=0}return $=t,A}function Ve(A,t){var r=0,a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0;e:{if((0|(r=e[A+8>>2]))==e[A+12>>2])if((a=e[A+4>>2])>>>0>(o=e[A>>2])>>>0)s=Yt((i=(1+(a-o>>2)|0)/-2<<2)+a|0,a,r=r-a|0)+r|0,e[A+8>>2]=s,e[A+4>>2]=i+e[A+4>>2];else{if((i=(0|r)==(0|o)?1:r-o>>1)>>>0>=1073741824)break e;if(p=(c=za(s=i<<2))+s|0,s=i=(-4&i)+c|0,(0|r)!=(0|a)){if(I=-4&(r=r-a|0),m=1+((f=r-4|0)>>>2|0)&7)for(s=0,r=i;e[r>>2]=e[a>>2],a=a+4|0,r=r+4|0,(0|m)!=(0|(s=s+1|0)););else r=i;if(s=i+I|0,!(f>>>0<28))for(;e[r>>2]=e[a>>2],e[r+4>>2]=e[a+4>>2],e[r+8>>2]=e[a+8>>2],e[r+12>>2]=e[a+12>>2],e[r+16>>2]=e[a+16>>2],e[r+20>>2]=e[a+20>>2],e[r+24>>2]=e[a+24>>2],e[r+28>>2]=e[a+28>>2],a=a+32|0,(0|s)!=(0|(r=r+32|0)););}e[A+12>>2]=p,e[A+8>>2]=s,e[A+4>>2]=i,e[A>>2]=c,o&&(xt(o),s=e[A+8>>2])}else s=r;return e[s>>2]=e[t>>2],void(e[A+8>>2]=e[A+8>>2]+4)}Zi(),xe()}function $e(A,t){var r,a=0,s=0,i=0,o=0,c=0;s=189088,J=r=J-320|0,e[r+312>>2]=0,i=mi(A,o=qA(r+112|0,189088),r,r+316|0,r+312|0,0),a=e[r+316>>2];e:if((0|t)<=3){if((0|a)<2)break e;if(i=3&(t=a-1|0),A=1,a-2>>>0>=3)for(c=-4&t,t=0;g[0|(a=A+r|0)]>=4&&(g[0|a]=3),g[1+(a=A+r|0)|0]>=4&&(g[a+1|0]=3),g[a+2|0]>=4&&(g[a+2|0]=3),g[a+3|0]>=4&&(g[a+3|0]=3),A=A+4|0,(0|c)!=(0|(t=t+4|0)););if(!i)break e;for(t=0;g[0|(a=A+r|0)]>=4&&(g[0|a]=3),A=A+1|0,(0|i)!=(0|(t=t+1|0)););}else if(A=1,!((0|a)<=1)){for(;;){if((0|i)>g[0|(c=A+r|0)]){if((0|a)!=(0|(A=A+1|0)))continue;break e}break}g[0|c]=t}if(A=l[0|o])for(t=1;a=e[144464+((255&A)<<2)>>2],l[a+11|0]!=2|16&l[a+6|0]||(i=255&(a=g[t+r|0]),(0|a)<2&&i||(g[0|s]=l[i+94151|0],s=s+1|0,A=l[0|o]),t=t+1|0),g[0|s]=A,s=s+1|0,A=l[0|(o=o+1|0)];);g[0|s]=0,J=r+320|0}function fA(A){var t=0,r=0,a=0,s=0,i=0,o=0;r=t=e[(A|=0)>>2],e[A>>2]=t+1;e:{A:{r:{s:{i:{c:{o:switch(((i=l[0|t])>>>4|0)-8|0){case 0:case 1:case 2:case 3:break A;case 7:break i;case 6:break c;case 4:case 5:break o;default:break e}if((a=t+2|0)>>>0>=(s=e[A+4>>2])>>>0)break s;if(e[A>>2]=a,(192&(r=l[r+1|0]))!=128)break r;return 63&r|i<<6&1984}if((a=t+3|0)>>>0>=(s=e[A+4>>2])>>>0)break s;if(r=t+2|0,e[A>>2]=r,(192&(t=l[t+1|0]))!=128){a=r;break r}if(e[A>>2]=a,(192&(r=l[0|r]))!=128)break r;return 63&r|(63&t|i<<6&960)<<6}if(!((s=e[A+4>>2])>>>0<=(r=t+4|0)>>>0)){if(a=t+2|0,e[A>>2]=a,(192&(s=l[t+1|0]))!=128||(a=t+3|0,e[A>>2]=a,(192&(o=l[t+2|0]))!=128)||(e[A>>2]=r,t=l[0|a],a=r,(192&t)!=128))break r;return 0|((A=63&t|o<<6&4032|(63&s|i<<6&960)<<12)>>>0>=1114112?65533:A)}}e[A>>2]=s;break A}e[A>>2]=a-1}i=65533}return 0|i}function NA(A,t,r,a){var s,i,o=0,c=0,f=0,m=0,p=0,I=0;if(J=s=J-432|0,!(!a|!(536870912&(i=TA(A,t,r,a))))&&(b[s+48>>1]=8192,a=qA(s+48|2,a),l[0|a])){for(f=s+224|0,o=1,m=200;;){if(Gt(s+44|0,a),t=hs(e[s+44>>2]),c=e[r>>2],t?(e[r>>2]=2|c,zr(Ht(e[s+44>>2]),a)):e[r>>2]=-3&c,I=e[33264],TA(A,a,r,0),1&o?(e[s+16>>2]=189088,c=as(f,m,84130,s+16|0)):(e[s+32>>2]=15,e[s+36>>2]=189088,c=as(f,m,84434,s+32|0)),o=(t=e[33264])+1|0,e[33264]=o,t>>>0<=2147483646){for(;;)if(t=a,a=a+1|0,(0|(p=g[0|t]))==32|p-9>>>0<5){for(;t=(a=t)+1|0,(0|(p=g[0|a]))==32|p-9>>>0<5;);if(o=o-1|0,e[33264]=o,!((0|o)>0))break}}if(f=f+c|0,e[33264]=I,!(l[0|a]&&(o=0,(0|(m=m-c|0))>1)))break}(s+224|0)!=(0|f)&&(e[s>>2]=s+224,as(189088,200,84130,s))}return J=s+432|0,i}function He(A,t,r,a,s,i,o,c){var f,m;f=e[32972],m=e[f+116>>2],b[A+8>>1]=M[A+8>>1]+i,i=32&c?0-i|0:i,b[A+10>>1]=i+M[A+10>>1],b[A+12>>1]=i+M[A+12>>1],a=(0|a)>(0|(i=((i=(0|z(t,m))/256|0)-(t=b[A+6>>1])|0)/2|0))?i:a,b[A+6>>1]=((0|r)<(0|a)?a:r)+t;e:{A:switch(s-1|0){case 0:t=(0|(t=235-(r=b[A+4>>1])|0))<=-100?-100:t,b[A+4>>1]=((0|t)>=-60?-60:t)+r;break e;case 1:t=(0|(t=(0|(t=235-(r=b[A+4>>1])|0))<=-300?-300:t))>=-150?-150:t,b[A+4>>1]=t+r,b[A+2>>1]=t+M[A+2>>1];break e;case 2:break A;default:break e}t=(0|(t=(0|(t=100-(r=b[A+4>>1])|0))<=-400?-400:t))>-300?-400:t,b[A+4>>1]=t+r,b[A+2>>1]=t+M[A+2>>1]}e[f+132>>2]||(g[A+20|0]=(z(l[A+20|0],o)>>>0)/100,g[A+21|0]=(z(l[A+21|0],o)>>>0)/100,g[A+22|0]=(z(l[A+22|0],o)>>>0)/100,g[A+23|0]=(z(l[A+23|0],o)>>>0)/100,g[A+24|0]=(z(l[A+24|0],o)>>>0)/100,g[A+25|0]=(z(l[A+25|0],o)>>>0)/100)}function VA(A){var t,r=0,a=0,s=0,i=0;J=t=J-48|0;e:{if(A){l[0|A]||(A=ai(84285),l[0|A]&&A||(A=ai(121696),l[0|A]&&A||(A=ai(84614),l[0|A]&&A||(A=84891))));A:{for(;;){if(!(!(a=l[A+r|0])|(0|a)==47)){if(s=23,(0|(r=r+1|0))!=23)continue;break A}break}s=r}a=84891;A:{r:{if(r=l[0|A],(l[A+s|0]|(0|r)==46||(a=A,(0|r)==67))&&!l[a+1|0]||!vr(a,84891)||!vr(a,85136)){if(r=121652,l[a+1|0]==46)break r;A=0;break A}if(r=e[56851])for(;;){if(!vr(a,r+8|0))break r;if(!(r=e[r+32>>2]))break}(A=Ct(36))&&(r=e[30414],e[A>>2]=e[30413],e[A+4>>2]=r,wt(r=A+8|0,a,s),g[r+s|0]=0,e[A+32>>2]=e[56851],e[56851]=A),r=A||121652}A=r}if((0|A)==-1)break e;e[56809]=A}else A=e[56809];i=A?A+8|0:84309}return J=t+48|0,i}function bA(A){var t=0,r=0,a=0,s=0,i=0,o=0,c=0,f=0,m=0;f=!!((t=e[A+112>>2])|(a=e[A+116>>2])),s=t,o=t=(i=e[A+4>>2])-(c=e[A+44>>2])|0,r=t+e[A+120>>2]|0,t=e[A+124>>2]+(t>>31)|0;e:{if(!(((0|(t=r>>>0<o>>>0?t+1|0:t))>=(0|a)&r>>>0>=s>>>0|(0|t)>(0|a))&f)){if((0|(f=Ui(A)))>=0)break e;i=e[A+4>>2],c=e[A+44>>2]}return e[A+112>>2]=-1,e[A+116>>2]=-1,e[A+104>>2]=i,a=(o=r)+(r=c-i|0)|0,t=(r>>31)+t|0,e[A+120>>2]=a,e[A+124>>2]=r>>>0>a>>>0?t+1|0:t,-1}return t=(a=r+1|0)?t:t+1|0,i=e[A+4>>2],c=e[A+8>>2],o=s=e[A+116>>2],s|(r=e[A+112>>2])&&(s=r-a|0,(0|(r=o-(t+(r>>>0<a>>>0)|0)|0))>=(0|(o=(m=c-i|0)>>31))&s>>>0>=m>>>0|(0|r)>(0|o)||(c=s+i|0)),e[A+104>>2]=c,a=(s=(r=e[A+44>>2])-i|0)+a|0,t=(s>>31)+t|0,e[A+120>>2]=a,e[A+124>>2]=a>>>0<s>>>0?t+1|0:t,r>>>0>=i>>>0&&(g[i-1|0]=f),f}function Yt(A,t,r){var a=0,s=0;e:if((0|A)!=(0|t)){if(t-(s=A+r|0)>>>0<=0-(r<<1)>>>0)return wt(A,t,r);if(a=3&(A^t),A>>>0<t>>>0){if(a)a=A;else{if(3&A)for(a=A;;){if(!r)break e;if(g[0|a]=l[0|t],t=t+1|0,r=r-1|0,!(3&(a=a+1|0)))break}else a=A;if(!(r>>>0<=3))for(;e[a>>2]=e[t>>2],t=t+4|0,a=a+4|0,(r=r-4|0)>>>0>3;);}if(r)for(;g[0|a]=l[0|t],a=a+1|0,t=t+1|0,r=r-1|0;);}else{if(!a){if(3&s)for(;;){if(!r)break e;if(g[0|(a=(r=r-1|0)+A|0)]=l[t+r|0],!(3&a))break}if(!(r>>>0<=3))for(;e[(r=r-4|0)+A>>2]=e[t+r>>2],r>>>0>3;);}if(!r)break e;for(;g[(r=r-1|0)+A|0]=l[t+r|0],r;);}}return A}function Vt(A,t,r,a){switch(t-9|0){case 0:return t=e[r>>2],e[r>>2]=t+4,void(e[A>>2]=e[t>>2]);case 6:return t=e[r>>2],e[r>>2]=t+4,t=b[t>>1],e[A>>2]=t,void(e[A+4>>2]=t>>31);case 7:return t=e[r>>2],e[r>>2]=t+4,e[A>>2]=M[t>>1],void(e[A+4>>2]=0);case 8:return t=e[r>>2],e[r>>2]=t+4,t=g[0|t],e[A>>2]=t,void(e[A+4>>2]=t>>31);case 9:return t=e[r>>2],e[r>>2]=t+4,e[A>>2]=l[0|t],void(e[A+4>>2]=0);case 16:return t=e[r>>2]+7&-8,e[r>>2]=t+8,void(D[A>>3]=D[t>>3]);case 17:ct[0|a](A,r);default:return;case 1:case 4:case 14:return t=e[r>>2],e[r>>2]=t+4,t=e[t>>2],e[A>>2]=t,void(e[A+4>>2]=t>>31);case 2:case 5:case 11:case 15:return t=e[r>>2],e[r>>2]=t+4,e[A>>2]=e[t>>2],void(e[A+4>>2]=0);case 3:case 10:case 12:case 13:}t=e[r>>2]+7&-8,e[r>>2]=t+8,r=e[t+4>>2],e[A>>2]=e[t>>2],e[A+4>>2]=r}function cr(A,t,r,a,s,i){var o;J=o=J-80|0;e:if((0|i)>=16384){if(PA(o+32|0,t,r,a,s,0,0,0,2147352576),a=e[o+40>>2],s=e[o+44>>2],t=e[o+32>>2],r=e[o+36>>2],i>>>0<32767){i=i-16383|0;break e}PA(o+16|0,t,r,a,s,0,0,0,2147352576),i=((0|i)>=49149?49149:i)-32766|0,a=e[o+24>>2],s=e[o+28>>2],t=e[o+16>>2],r=e[o+20>>2]}else(0|i)>-16383||(PA(o- -64|0,t,r,a,s,0,0,0,7471104),a=e[o+72>>2],s=e[o+76>>2],t=e[o+64>>2],r=e[o+68>>2],i>>>0>4294934644?i=i+16269|0:(PA(o+48|0,t,r,a,s,0,0,0,7471104),i=((0|i)<=-48920?-48920:i)+32538|0,a=e[o+56>>2],s=e[o+60>>2],t=e[o+48>>2],r=e[o+52>>2]));PA(o,t,r,a,s,0,0,0,i+16383<<16),t=e[o+12>>2],e[A+8>>2]=e[o+8>>2],e[A+12>>2]=t,t=e[o+4>>2],e[A>>2]=e[o>>2],e[A+4>>2]=t,J=o+80|0}function sr(A,t){var r,a,s=0;J=r=J+-64|0,s=e[A>>2],a=e[s-4>>2],s=e[s-8>>2],e[r+32>>2]=0,e[r+36>>2]=0,e[r+40>>2]=0,e[r+44>>2]=0,e[r+48>>2]=0,e[r+52>>2]=0,g[r+55|0]=0,g[r+56|0]=0,g[r+57|0]=0,g[r+58|0]=0,g[r+59|0]=0,g[r+60|0]=0,g[r+61|0]=0,g[r+62|0]=0,e[r+24>>2]=0,e[r+28>>2]=0,e[r+20>>2]=0,e[r+16>>2]=125084,e[r+12>>2]=A,e[r+8>>2]=t,A=A+s|0,s=0;e:if(_a(a,t,0))e[r+56>>2]=1,ct[e[e[a>>2]+20>>2]](a,r+8|0,A,A,1,0),s=e[r+32>>2]==1?A:0;else{ct[e[e[a>>2]+24>>2]](a,r+8|0,A,1,0);A:switch(e[r+44>>2]){case 0:s=e[r+48>>2]==1&&e[r+36>>2]==1&&e[r+40>>2]==1?e[r+28>>2]:0;break e;case 1:break A;default:break e}e[r+32>>2]!=1&&e[r+48>>2]|e[r+36>>2]!=1|e[r+40>>2]!=1||(s=e[r+24>>2])}return J=r- -64|0,s}function jt(A,t,r,a,s){var i,o,c=0;J=i=J-80|0,b[i+72>>1]=0,e[i+64>>2]=0,e[i+68>>2]=0,g[0|a]=0,c=zr(t,o=2|(c=i- -64|0))+c|0,g[c+2|0]=32;e:if((0|r)!=-1)t>>>0>=33&&!Or(t)?(g[c+3|0]=(0|r)==32?32:31,g[i+65|0]=95,RA(A,i- -64|1,i+16|0)||(g[i+65|0]=32,RA(A,o,i+16|0)||qt(A,o,i+16|0,40,0,268435456,0)),l[i+16|0]||tA(A,t,i+16|0),t=qA(a,i+16|0),!(r=l[0|t])|(0|r)==21||(e[i+56>>2]=0,e[i+60>>2]=0,$t(A,t,i+56|0,-1,1&s))):(e[i>>2]=t,DA(t=i- -64|1,85485,i),RA(A,t,a));else{if(RA(A,o,a)||(g[i+65|0]=95,RA(A,i- -64|1,i+16|0)|e[A+212>>2]==25966))break e;ci(85055),RA(e[47194],o,i+16|0)&&(g[0|a]=21,g[a+1|0]=0),ha(e[e[32972]+60>>2])}J=i+80|0}function jA(A,t,r){var a=0,s=0,i=0,o=0;if(r&&(g[0|A]=t,g[(a=A+r|0)-1|0]=t,!(r>>>0<3||(g[A+2|0]=t,g[A+1|0]=t,g[a-3|0]=t,g[a-2|0]=t,r>>>0<7||(g[A+3|0]=t,g[a-4|0]=t,r>>>0<9||(s=(a=0-A&3)+A|0,t=z(255&t,16843009),e[s>>2]=t,e[(r=(a=r-a&-4)+s|0)-4>>2]=t,a>>>0<9||(e[s+8>>2]=t,e[s+4>>2]=t,e[r-8>>2]=t,e[r-12>>2]=t,a>>>0<25||(e[s+24>>2]=t,e[s+20>>2]=t,e[s+16>>2]=t,e[s+12>>2]=t,e[r-16>>2]=t,e[r-20>>2]=t,e[r-24>>2]=t,e[r-28>>2]=t,(r=a-(o=4&s|24)|0)>>>0<32))))))))for(a=It(t,0,1,1),i=$,t=s+o|0;e[t+24>>2]=a,e[t+28>>2]=i,e[t+16>>2]=a,e[t+20>>2]=i,e[t+8>>2]=a,e[t+12>>2]=i,e[t>>2]=a,e[t+4>>2]=i,t=t+32|0,(r=r-32|0)>>>0>31;);return A}function KA(){var A,t=0,r=0,a=0,s=0,i=0;if(J=A=J-208|0,(0|(a=e[50303]))>0)for(;(s=e[(r=201216+(t<<2)|0)>>2])&&(xt(s),e[r>>2]=0),(0|a)!=(0|(t=t+1|0)););if(e[50303]=0,e[A+16>>2]=137584,e[A+20>>2]=47,DA(t=A+32|0,87827,A+16|0),_s(t,HA(t)+1|0,0),e[A+4>>2]=47,e[A>>2]=137584,DA(t,87933,A),_s(t,HA(t)+1|0,1),t=e[50303],e[201216+(r=t<<2)>>2]=0,r=Ur(a=e[50741],r+4|0)){if(e[50741]=r,Ai(201216,t,7),a=e[50741],r=0,t=e[50304])for(s=0;i=e[t+4>>2],l[0|i]&&vr(i+1|0,86589)&&xa(e[t+8>>2],88032,3)&&(e[(r<<2)+a>>2]=t,r=r+1|0),t=e[201216+((s=s+1|0)<<2)>>2];);e[(r<<2)+a>>2]=0}return J=A+208|0,a}function Jt(A,t,r){var a=0,s=0,i=0,o=0;if(i=t-1|0,(0|t)>=2){t=A;e:{for(;;){A:{r:{if((0|(a=e[r+4>>2]))!=(0|(s=e[r+8>>2]))){if((o=ri(a,10,s-a|0))?s=1+(o-(a=e[r+4>>2])|0)|0:(a=e[r+4>>2],s=e[r+8>>2]-a|0),wt(t,a,a=s>>>0<i>>>0?s:i),s=a+e[r+4>>2]|0,e[r+4>>2]=s,t=t+a|0,o||!(i=i-a|0))break A;if((0|s)!=e[r+8>>2]){e[r+4>>2]=s+1,a=l[0|s];break r}}if(!((0|(a=Ui(r)))>=0)){if(a=0,(0|A)==(0|t))break e;if(16&l[0|r])break A;break e}}if(g[0|t]=a,t=t+1|0,(255&a)!=10&&(i=i-1|0))continue}break}A?(g[0|t]=0,a=A):a=0}}else if(t=e[r+72>>2],e[r+72>>2]=t-1|t,!i)return g[0|A]=0,A;return a}function La(A){var t=0,r=0,a=0,s=0,i=0,o=0,c=0,f=0;if(t=z(A,44),(0|(A=e[t+137896>>2]))>0&&La(A-1|0),A=e[36115],!((0|(t=e[36+(r=t+137856|0)>>2]))<=0)){if(s=e[r+32>>2],c=1&t,(0|t)!=1)for(f=-2&t,r=0;t=l[10+(i=(a=r<<4)+s|0)|0],e[144464+(t<<2)>>2]=i,(0|A)>=(0|t)?t=A:jA(144464+((A=A+1|0)<<2)|0,0,t-A<<2),A=l[10+(a=(16|a)+s|0)|0],e[144464+(A<<2)>>2]=a,(0|A)<=(0|t)?A=t:jA(144464+((t=t+1|0)<<2)|0,0,A-t<<2),r=r+2|0,(0|f)!=(0|(o=o+2|0)););else r=0;c&&(t=l[10+(r=(r<<4)+s|0)|0],e[144464+(t<<2)>>2]=r,(0|A)>=(0|t)||(jA(144464+((A=A+1|0)<<2)|0,0,t-A<<2),A=t))}e[36115]=A}function fa(A,t,r,a,s){var i,o=0,c=0,f=0;J=i=J-16|0;e:if(1&g[A+106|0]&&(o=l[0|r],!(!(1&g[a+2|0])&(0|o)!=46||256&(f=e[a+12>>2])|!(!(2&f)||s)||(Gt(i+12|0,(0|o)!=46?r:r+2|0),!(o=l[0|r])|!l[r+1|0])))){if(!(!(f=e[i+12>>2])|2&l[a+2|0])){if(!rr(f))break e;o=l[0|r]}(0|o)==46&&(g[0|r]=32),c=2,e[A+212>>2]!=26741|s||(r=rr(e[i+12>>2])?NA(A,r+2|0,0,0):0,128&l[A+8233|0]&&(!(!(s=e[i+12>>2])|2&l[a+2|0])&s-48>>>0>=10||(c=0)),c=32768&r?0:c,131072&r&&(c=163840&e[A+8232>>2]?34:l[t-2|0]!=45?c:0))}return J=i+16|0,c}function ir(A,t,r,a,s){var i,o=0,c=0;if(J=i=J-208|0,e[i+204>>2]=r,jA(r=i+160|0,0,40),e[i+200>>2]=e[i+204>>2],(0|$A(0,t,i+200|0,i+80|0,r,a,s))<0)s=-1;else{e[A+76>>2],o=e[A>>2],e[A+72>>2]<=0&&(e[A>>2]=-33&o);e:{A:{if(e[A+48>>2]){if(e[A+16>>2])break A}else e[A+48>>2]=80,e[A+28>>2]=0,e[A+16>>2]=0,e[A+20>>2]=0,c=e[A+44>>2],e[A+44>>2]=i;if(r=-1,yn(A))break e}r=$A(A,t,i+200|0,i+80|0,i+160|0,a,s)}c&&(ct[e[A+36>>2]](A,0,0),e[A+48>>2]=0,e[A+44>>2]=c,e[A+28>>2]=0,t=e[A+20>>2],e[A+16>>2]=0,e[A+20>>2]=0,r=t?r:-1),t=A,A=e[A>>2],e[t>>2]=A|32&o,s=32&A?-1:r}return J=i+208|0,s}function rt(A,t,r,a){var s,i=0,o=0,c=0,f=0,m=0,p=0,I=0;if(J=s=J-208|0,o=l[0|t])for(;g[i+s|0]=o,f=((255&o)==6&(0|c)!=21)+f|0,c=o<<24>>24,o=l[(i=i+1|0)+t|0];);if(g[i+s|0]=0,i=l[0|s])for(p=f-2|0,c=0,I=(0|a)<2,o=0;;){e:{A:if((255&i)!=6|I|(0|o)==21){if((0|(a=255&i))==255){if(!m|(0|r)<2)break e;a=r>>>0>2?11:(0|c)%3|0?23:11}o=a,a=c}else{if(i=c+1|0,l[A+169|0]){o=(0|i)>1?5:6,a=i;break A}if(o=6,a=f,(0|i)==(0|f))break A;o=(0|i)%3|0||(0|c)==(0|p)?5:6,a=i}c=a,g[0|t]=o,t=t+1|0}if(!(i=l[(m=m+1|0)+s|0]))break}(0|r)>=2&&(g[0|t]=11,t=t+1|0),g[0|t]=0,J=s+208|0}function dr(A,t){var r=0;r=0,A&&(r=e[50754],r=(A=(A=(0|z(e[145712+(t?12:((0|A)>199)<<2)>>2],A))/256|0)>>>0>(t=e[36430])>>>0?A:t)>>>0<=89999?(z(A,r)>>>0)/1e3|0:(z(A,(0|r)/25|0)>>>0)/40|0),(0|(A=e[36440]))<=0||(0|(t=e[36424]))<0||(e[4+(t=216192+(t<<4)|0)>>2]||(e[t+4>>2]=A),e[36440]=0),e[36426]=0,e[36439]=-1,e[36455]=e[50758],Oa(),e[36427]=-1,A=216192+(e[50758]<<4)|0,e[A>>2]=5,e[A+4>>2]=r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,e[36426]=0,e[36438]&&(e[36438]=0,A=216192+(e[50758]<<4)|0,e[A>>2]=14,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0)}function gA(A,t,r,a,s,i,o,c){var f,m=0,p=0,I=0;m=1,f=p=2147483647&a;e:if(!((I=(0|p)==2147418112)&!r?A|t:I&!!(0|r)|p>>>0>2147418112)&&!((I=(0|(p=2147483647&c))==2147418112)&!o?s|i:I&!!(0|o)|p>>>0>2147418112)){if(!(A|s|r|o|t|i|p|f))return 0;if((0|(m=a&c))>0|(0|m)>=0){if(m=-1,(0|r)==(0|o)&(0|a)==(0|c)?(0|t)==(0|i)&A>>>0<s>>>0|t>>>0<i>>>0:r>>>0<o>>>0&(0|a)<=(0|c)|(0|a)<(0|c))break e;return!!(A^s|r^o|t^i|a^c)}m=-1,((0|r)==(0|o)&(0|a)==(0|c)?(0|t)==(0|i)&A>>>0>s>>>0|t>>>0>i>>>0:r>>>0>o>>>0&(0|a)>=(0|c)|(0|a)>(0|c))||(m=!!(A^s|r^o|t^i|a^c))}return m}function JA(A,t){var r=0,a=0,s=0,i=0;for(r=l[85836]|l[85837]<<8,g[0|t]=r,g[t+1|0]=r>>>8,g[t+2|0]=l[85838];;)if(s=l[0|A],A=r=A+1|0,(0|s)!=255){if(!s)break;if(!(a=e[144464+(s<<2)>>2]))continue;if(l[a+11|0]==1&&(i=l[a+14|0],!(M[a+8>>1]|i>>>0>4))){if(i>>>0<2)continue;g[0|t]=l[i+93943|0],t=t+1|0;continue}if(255&(A=e[a>>2]))for(;g[0|t]=A,t=t+1|0,a=65280&A,A=A>>>8|0,a;);if(A=r,(0|s)!=21||(32|(r=g[0|A]))-97>>>0>=26)continue;for(;g[0|t]=r,t=t+1|0,(32|(r=g[0|(A=A+1|0)]))-97>>>0<26;);}g[0|t]=0}function pa(A,t){var r,a,s=0,i=0,o=0;J=r=J-112|0,A||(e[50303]||KA(),A=201216),ar(s=r+16|0,t,40),e[r>>2]=47,e[r+4>>2]=s,DA(t=r- -64|0,87599,r),a=HA(t),o=-1;e:{A:{r:{if(i=e[A>>2]){t=0,s=-1;s:{for(;;){if(hn(r+16|0,e[i>>2])){if(hn(r+16|0,i=e[i+8>>2])?o=hn(r- -64|0,i+(HA(i)-a|0)|0)?o:t:s=t,i=e[((t=t+1|0)<<2)+A>>2])continue;break s}break}if((0|t)>=0)break A;t=(0|s)<0?o:s;break r}if((0|(t=s))>=0)break r}t=o}if(s=0,(0|t)<0)break e}s=e[(t<<2)+A>>2]}return J=r+112|0,s}function Nr(A,t){var r,a,s=0,i=0,o=0,c=0,f=0;J=r=J-16|0,be(+t),a=0|X(1),i=0|X(0),(0|(o=(s=2147483647&a)-1048576|0))==2145386495|o>>>0<2145386495?(c=i<<28,o=s>>>4|0,s=(15&s)<<28|i>>>4,i=o+1006632960|0):(0|s)==2146435072|s>>>0>2146435072?(c=i<<28,s=(15&a)<<28|i>>>4,i=a>>>4|2147418112):s|i?(ea(r,i,o=s,0,0,(s=s?Ye(s):Ye(i)+32|0)+49|0),f=e[r>>2],c=e[r+4>>2],o=15372-s<<16,s=e[r+8>>2],i=o|65536^e[r+12>>2]):(s=0,i=0),e[A>>2]=f,e[A+4>>2]=c,e[A+8>>2]=s,e[A+12>>2]=-2147483648&a|i,J=r+16|0}function Rr(A,t,r){var a=0,s=0;e:{A:{r:{if(!(3&((s=A)^t))){a=!!(0|r);s:if(!(!(3&t)|!r))for(;;){if(a=l[0|t],g[0|s]=a,!a)break e;if(s=s+1|0,a=!!(0|(r=r-1|0)),!(3&(t=t+1|0)))break s;if(!r)break}if(!a)break A;if(!l[0|t])break e;if(!(r>>>0<4))for(;;){if(~(a=e[t>>2])&a-16843009&-2139062144)break r;if(e[s>>2]=a,s=s+4|0,t=t+4|0,!((r=r-4|0)>>>0>3))break}}if(!r)break A}for(;;){if(a=l[0|t],g[0|s]=a,!a)break e;if(s=s+1|0,t=t+1|0,!(r=r-1|0))break}}r=0}return jA(s,0,r),A}function Pr(A,t,r,a,s,i){var o,c=0,f=0,m=0;J=o=J-240|0,c=e[r>>2],e[o+232>>2]=c,r=e[r+4>>2],e[o>>2]=A,e[o+236>>2]=r,m=1;e:{A:{r:{if(r|(0|c)!=1){for(c=A;;){if((0|Gs(r=c-e[(f=(a<<2)+i|0)>>2]|0,A,t))<=0){r=c;break r}s:{if(!((0|a)<2|s)&&(s=e[f-8>>2],(0|Gs(f=c-4|0,r,t))>=0||(0|Gs(f-s|0,r,t))>=0))break s;if(e[(m<<2)+o>>2]=r,Bi(c=o+232|0,s=Dn(c)),m=m+1|0,a=a+s|0,s=0,c=r,e[o+236>>2]|e[o+232>>2]!=1)continue;break A}break}r=c;break A}r=A}if(s)break e}js(o,m),In(r,t,a,i)}J=o+240|0}function Cr(A,t){var r=0;e[4+(134912+(r=A<<2)|0)>>2]=t,e[r+136192>>2]=t,r=28;e:{A:{r:switch(A-1|0){case 0:e[50792]=t,e[50786]=t,Os(3);break A;case 1:e[50787]=t,e[33037]=(0|z(l[e[50797]+105596|0],(0|z(e[50787],55))/100|0))/16;break A;case 2:A=(0|t)>=99?99:t,e[50785]=(0|A)>0?A:0;break A;case 3:e[50788]=(0|t)>=99?99:t;break A;case 12:e[47268]=t;break A;case 6:e[47205]=t;break A;case 9:break A;case 8:break r;default:break e}(A=255&t)&&(e[e[47192]+152>>2]=A),e[47196]=t}r=0}return r}function Ut(A,t){var r=0,a=0,s=0;e:if(e[A>>2])for(;;){if(Or(e[A-4>>2])){if(a=0,(0|(r=g[0|t]))==e[A>>2])for(;(0|(r=g[(a=a+1|0)+t|0]))==e[(A=A+4|0)>>2];);if(!r){for(;t=A,A=A+4|0,Or(e[t>>2]););for(s=t+((e[t>>2]==61)<<2)|0;s=(A=s)+4|0,Or(e[A>>2]););switch((t=e[A>>2])-34|0){case 0:case 5:break e}return Or(t)||e[A>>2]==47?102808:A}}if(!e[(A=A+4|0)>>2])break}return s}function xr(A,t,r,a){var s,i,o=0,c=0,f=0;return!l[A+25|0]|D[A+8>>3]!=r|D[A+16>>3]!=a?(D[A+16>>3]=a,D[A+8>>3]=r,a=(o=Pt(-3.141592653589793/(c=+e[A>>2])*a))*-o,D[A+48>>3]=a,o*=Ga(-6.283185307179586/c*r),o+=o,D[A+40>>3]=o,c=1-o-a,D[A+32>>3]=c,!(f=l[A+24|0])|r==0||(c=1/c,D[A+32>>3]=c,a*=r=-c,D[A+48>>3]=a,o*=r,D[A+40>>3]=o,f=1)):(f=l[A+24|0],a=D[A+48>>3],o=D[A+40>>3],c=D[A+32>>3]),g[A+25|0]=1,r=D[A+64>>3],s=D[A+56>>3],D[A+64>>3]=s,i=t,t=a*r+(c*t+o*s),D[A+56>>3]=f?i:t,t}function ti(A,t,r,a,s){var i,o=0,c=0,f=0,m=0;if((i=e[34388])&&!((0|(c=e[34436]))>=(e[34393]-2|0))){if(e[34436]=c+1,o=z(c,36)+i|0,e[o>>2]=A,e[o+4>>2]=e[34437],m=e[34438],e[o+12>>2]=t>>>24,e[o+8>>2]=16777215&t,e[o+24>>2]=m,t=e[50754],s=e[34439]+((s-e[34392]|0)/2|0)|0,e[o+20>>2]=s,t=Ne(f=1e3*+(0|s)/+(0|t))<2147483648?~~f:-2147483648,e[o+16>>2]=t,A-3>>>0<=1)return void(e[28+(z(c,36)+i|0)>>2]=e[33282]+r);t=28+(z(c,36)+i|0)|0,e[t>>2]=r,(0|A)==7&&(e[t+4>>2]=a)}}function Ya(A,t,r,a,s,i){var o=0,c=0,f=0,m=0;64&i?(t=31&(r=i+-64|0),(63&r)>>>0>=32?(r=0,t=s>>>t|0):(r=s>>>t|0,t=((1<<t)-1&s)<<32-t|a>>>t),a=0,s=0):i&&(f=a,o=31&(c=64-i|0),(63&c)>>>0>=32?(c=f<<o,m=0):(c=(1<<o)-1&f>>>32-o|s<<o,m=f<<o),f=t,t=31&i,(63&i)>>>0>=32?(o=0,t=r>>>t|0):(o=r>>>t|0,t=((1<<t)-1&r)<<32-t|f>>>t),t|=m,r=o|c,o=a,a=31&i,(63&i)>>>0>=32?(c=0,a=s>>>a|0):(c=s>>>a|0,a=((1<<a)-1&s)<<32-a|o>>>a),s=c),e[A>>2]=t,e[A+4>>2]=r,e[A+8>>2]=a,e[A+12>>2]=s}function Fi(A){var t=0,r=0;if(!A){if(e[33174]&&(t=Fi(e[33174])),e[33136]&&(t=Fi(e[33136])|t),A=e[56816])for(;e[A+20>>2]!=e[A+28>>2]&&(t=Fi(A)|t),A=e[A+56>>2];);return t}return e[A+76>>2],e[A+20>>2]==e[A+28>>2]||(ct[e[A+36>>2]](A,0,0),e[A+20>>2])?((0|(t=e[A+8>>2]))!=(0|(r=e[A+4>>2]))&&(t=r-t|0,ct[e[A+40>>2]](A,t,t>>31,1)),t=0,e[A+28>>2]=0,e[A+16>>2]=0,e[A+20>>2]=0,e[A+4>>2]=0,e[A+8>>2]=0):t=-1,t}function Ma(A,t){var r=0,a=0,s=0,i=0,o=0;if((192&(r=l[0|t]))==128)for(;(192&(r=l[0|(t=t-1|0)]))==128;);e:if(128&(r=r<<24>>24)){if(a=1,(0|(s=224&r))!=192)if((240&r)!=224){if(a=3,(248&r)!=240){r&=255,a=0;break e}}else a=2,o=1;r=l[a+93846|0]&r,(i=l[t+1|0])?(r=63&i|r<<6,(0|s)!=192&&((s=l[t+2|0])?(r=63&s|r<<6,o||((t=l[t+3|0])?r=63&t|r<<6:a=2)):a=1)):a=0}return e[A>>2]=r,a+1|0}function Si(A,t,r,a){var s,i=0,o=0;return 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o=0,c=0,f=0;64&i?(a=t,t=31&(s=i+-64|0),(63&s)>>>0>=32?(s=a<<t,a=0):(s=(1<<t)-1&a>>>32-t|r<<t,a<<=t),t=0,r=0):i&&(o=a,a=31&i,(63&i)>>>0>=32?(c=o<<a,f=0):(c=(1<<a)-1&o>>>32-a|s<<a,f=o<<a),o=t,a=31&(s=64-i|0),(63&s)>>>0>=32?(s=0,t=r>>>a|0):(s=r>>>a|0,t=((1<<a)-1&r)<<32-a|o>>>a),a=f|t,s|=c,t=31&i,(63&i)>>>0>=32?(c=o<<t,t=0):(c=(1<<t)-1&o>>>32-t|r<<t,t=o<<t),r=c),e[A>>2]=t,e[A+4>>2]=r,e[A+8>>2]=a,e[A+12>>2]=s}function Wa(A){var t=0;return A>>>0<=55295?t=l[e[125552+(A>>>6&67108860)>>2]+(255&A)|0]:(t=4,A>>>0<57344||(A>>>0<63488?t=3:A>>>0<=195327?t=l[e[126416+(A-63488>>>6&67108860)>>2]+(255&A)|0]:(t=2,A>>>0<917504||(A>>>0<=918015?t=l[e[128476+(A-917504>>>6&67108860)>>2]+(255&A)|0]:A>>>0<983040||(A>>>0<1048574?t=3:A>>>0<1048576||(t=3,A>>>0<1114110||(t=A>>>0<1114112?2:5))))))),255&t}function s0(A,t){var r=0,a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0;if(!((0|(r=e[33709]))<=0)){if(s=(0|A)>31?A-32|0:A,A=0,r>>>0>=4)for(p=-4&r;c=2|A,f=1|A,a=e[134912+((o=3|A)<<6)>>2]==(0|s)?o:e[134912+(c<<6)>>2]==(0|s)?c:e[134912+(f<<6)>>2]==(0|s)?f:e[134912+(A<<6)>>2]==(0|s)?A:a,A=A+4|0,(0|p)!=(0|(i=i+4|0)););if(i=3&r)for(;a=e[134912+(A<<6)>>2]==(0|s)?A:a,A=A+1|0,(0|i)!=(0|(m=m+1|0)););(0|a)<=0||(e[33709]=a,r=a)}qa(t,r)}function i0(A){var t,r=0,a=0,s=0;for(J=t=J-96|0,ar(t,A,60),Ni(t,1);s=Ht(g[0|(a=t+r|0)]),g[0|a]=s,r=r+1|0,255&s;);e[t+92>>2]=0,e[t+84>>2]=0,e[t+88>>2]=0,e[t+76>>2]=0,e[t+80>>2]=0,e[t+72>>2]=A;e:{A:{r:{if(Tt(t,1)){if(l[202976])break r;break A}if(e[50303]||KA(),r=268437247,!(A=pa(201216,t))||!Tt(e[A+8>>2],0))break e;if(!l[202976])break A}Tt(202976,2)}Tn(e[32972]),e[t+76>>2]=e[32972]+40,bi(t+72|0,202976),r=0}return J=t+96|0,r}function n0(A,t){if(!A)return 0;e:{A:{if(A){if(t>>>0<=127)break A;if(e[e[56841]>>2]){if(t>>>0<=2047){g[A+1|0]=63&t|128,g[0|A]=t>>>6|192,A=2;break 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r,a=0;J=r=J+-64|0,g[202976]=0,e[r+48>>2]=47,DA(r+59|0,91351,r+48|0),t||(g[r+59|0]=0);e:{A:{if(A&&(A=$a(A,43))){if(g[0|A]=0,g[0|(A=A+1|0)]-48>>>0>=10)break A;a=Vs(A)}if((0|a)<=0)break e;if(a>>>0<=9){e[r+4>>2]=a,e[r>>2]=r+59,DA(202976,91378,r);break e}e[r+20>>2]=a-10,e[r+16>>2]=r+59,DA(202976,91503,r+16|0);break e}e[r+36>>2]=A,e[r+32>>2]=r+59,DA(202976,85425,r+32|0)}J=r- -64|0}function Qa(A){var t,r=0,a=0,s=0;J=t=J-80|0,r=cs(A,t+12|0),e[t+12>>2]?(ar(a=t+16|0,r,60),r=0,Ni(a,1),!Tt(a,0)|!l[202976]||Tt(202976,2),Tn(e[32972]),bi(A,86012)):r=268437247,J=t+80|0;e:{A:{r:{s:{if((0|r)<=268437502){if(!r)break e;if((0|r)==268436479)break A;if((0|r)!=268437247)break s;return 2}if((0|r)==268437503|(0|r)==268437759)break r;if((0|r)==268439295)break e}return-1}return 2}s=1}return s}function Kr(A,t,r,a,s,i,o,c,f){var m,p,I;f=It(t,r,c,f),c=$,s=It(a,s,i,o),a=$+c|0,c=s>>>0>(f=s+f|0)>>>0?a+1|0:a,m=o,p=r,o=(r=It(o,s=0,r,a=0))+f|0,f=$+c|0,I=o,r=r>>>0>o>>>0?f+1|0:f,o=It(i,0,t,0),c=$,a=It(i,f=0,p,a),i=$+f|0,i=a>>>0>(c=c+a|0)>>>0?i+1|0:i,a=r,i=i>>>0>(f=i+I|0)>>>0?a+1|0:a,r=It(t,0,m,s)+c|0,s=$,c=(s=r>>>0<c>>>0?s+1|0:s)+f|0,f=i,e[A+8>>2]=c,e[A+12>>2]=s>>>0>c>>>0?f+1|0:f,e[A>>2]=o,e[A+4>>2]=r}function qA(A,t){var r=0,a=0;e:{if(3&((a=A)^t))r=l[0|t];else{if(3&t)for(;;){if(r=l[0|t],g[0|a]=r,!r)break e;if(a=a+1|0,!(3&(t=t+1|0)))break}if(!(~(r=e[t>>2])&r-16843009&-2139062144))for(;e[a>>2]=r,r=e[t+4>>2],a=a+4|0,t=t+4|0,!(r-16843009&~r&-2139062144););}if(g[0|a]=r,255&r)for(;r=l[t+1|0],g[a+1|0]=r,a=a+1|0,t=t+1|0,r;);}return A}function Ga(A){var t,r=0,a=0;J=t=J-16|0,be(+A),a=0|X(1),X(0);e:if((a&=2147483647)>>>0<=1072243195){if(r=1,a>>>0<1044816030)break e;r=ki(A,0)}else if(r=A-A,!(a>>>0>=2146435072)){switch(3&Ia(A,t)){case 0:r=ki(D[t>>3],D[t+8>>3]);break e;case 1:r=-Es(D[t>>3],D[t+8>>3],1);break e;case 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t=0;e[A+296>>2]=303173648,e[A+300>>2]=370677780,t=e[26341],e[A+304>>2]=e[26340],e[A+308>>2]=t,t=e[26343],e[A+312>>2]=e[26342],e[A+316>>2]=t,xs(A),e[A+56>>2]=2,e[A+36>>2]=3,e[A+40>>2]=1074,g[A+168|0]=5,e[A+132>>2]=32,e[A+104>>2]=1032,e[A+108>>2]=66,e[A+8>>2]=5,e[A+12>>2]=32,g[A+365|0]=64|l[A+365|0],g[A+368|0]=64|l[A+368|0],g[A+396|0]=64|l[A+396|0],g[A+399|0]=64|l[A+399|0]}function c0(A,t,r){var a=0,s=0,i=0;e:{if(!(a=e[r+16>>2])){if(yn(r))break e;a=e[r+16>>2]}if(a-(i=e[r+20>>2])>>>0<t>>>0)return 0|ct[e[r+36>>2]](r,A,t);A:if(e[r+80>>2]<0)a=0;else{for(s=t;;){if(!(a=s)){a=0;break A}if(l[(s=a-1|0)+A|0]==10)break}if((s=0|ct[e[r+36>>2]](r,A,a))>>>0<a>>>0)break e;A=A+a|0,t=t-a|0,i=e[r+20>>2]}wt(i,A,t),e[r+20>>2]=e[r+20>>2]+t,s=t+a|0}return s}function g0(A){var t,r=0,a=0;e:{if((0|(t=e[34064]))>0)for(;;){if((a=e[136284+(r<<4)>>2])&&!vr(A,a)){if(e[136276+(r<<4)>>2])return r;if(a=-1,$s(0,r))break e;return 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RAHAgIQEBAQEBAaGhoXEwcQEBAQEBAQEBAQGhoaFxMCBwcHBwcHBwcHBwcHBwICAhgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYAgICAgICAgICAgICAgICAg0NDQ0NDQ0NDQ0NDQ0MDAwMDQwMDA0NDQ0NDQ0NDQ0NDQICAgICAgICAgICAgICAhsbChsbGxsKGxsGCgoKBgYKCgoGGwobGxoKCgoKChsbGxsbGwobChsKGwoKCgobBgoKCgoGCAgICAYbGwYGCgoaGhoaGgoGBgYGGxobGwYbEBAQEBAQEBAQEBAQEBAQEA8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PCgYPDw8PEBsbAgICAhoaGhoaGxsbGxsaGhsbGxsaGxsaGxsaGxsbGxsbGxobGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGhobGxobGhsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGxsbGxsbGxsXExcTGxsbGxsbGxsbGxsbGxsbGxsbGxsaGhsbGxsbGxsXExsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxobGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxoaGhoaGhsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGwICAgICAgICAgICAgICAgICAgICAgICAgIbGxsbGxsbGxsbGwICAgICAgICAgICAgICAgICAgICAhAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxAQEBAQEBAQEBAQEBAQEBAQEBAQEBAbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsaGxsbGxsbGxsbGhsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxoaGhoaGhoaGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGhsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbFxMXExcTFxMXExcTFxMQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxoaGhoaFxMaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaFxMXExcTFxMXExoaGhoaGhoaGhoaGhoaGhobGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoXExcTFxMXExcTFxMXExcTFxMXExcTGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaFxMXExoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaFxMaGhsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxoaGhoaGhoaGhoaGhoaGhoaGhoaGhsbGhoaGhoaGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbAgIbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGwICGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGwIbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGwIKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgIGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgIKBgoKCgYGCgYKBgoGCgoKCgYKBgYKBgYGBgYGBwcKCgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYGGxsbGxsbCgYKBg0NDQoGAgICAgIWFhYWEBYWBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYCBgICAgICBgICCAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCAgICAgICBxYCAgICAgICAgICAgICAg0ICAgICAgICAgICAgICAgICAgICAgICAICAgICAgICAggICAgICAgCCAgICAgICAIICAgICAgIAggICAgICAgCCAgICAgICAIICAgICAgIAggICAgICAgCCAgICAgICAINDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDRYWFRQVFBYWFhUUFhUUFhYWFhYWFhYWEhYWEhYVFBYWFRQXExcTFxMXExYWFhYWBxYWFhYWFhYWFhYSEhYWFhYSFhcWFhYWFhYWFhYWFhYCAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsCGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsCAgICAgICAgICAgIbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbAgICAgICAgICAgICAgICAgICAgICAgICAgIbGxsbGxsbGxsbGxsCAgICHhYWFhsHCA8XExcTFxMXExcTGxsXE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I=8&s;if(T=-1,(0|((L=I|E)?2147483645:2147483646))<(0|E))break e;if(Z=1+(!!(0|L)+E|0)|0,(0|(f=-33&i))!=70){if((Qe-(c=fs(((c=a>>31)^a)-c|0,0,Qe))|0)<=1)for(;g[0|(c=c-1|0)]=48,(Qe-c|0)<2;);if(g[0|(ve=c-2|0)]=i,g[c-1|0]=(0|a)<0?45:43,(0|(c=Qe-ve|0))>(2147483647^Z))break e}else{if((2147483647^Z)<(0|a))break e;c=(0|a)>0?a:0}if((0|(a=c+Z|0))>(2147483647^he))break e;Aa(A,32,r,Z=a+he|0,s),Zr(A,rA,he),Aa(A,48,r,Z,65536^s);r:{s:{i:{if((0|f)==70){for(a=8|(i=o+16|0),I=9|i,m=f=m>>>0>ee>>>0?ee:m;;){c=fs(e[m>>2],0,I);c:if((0|f)==(0|m))(0|c)==(0|I)&&(g[o+24|0]=48,c=a);else{if(o+16>>>0>=c>>>0)break c;for(;g[0|(c=c-1|0)]=48,o+16>>>0<c>>>0;);}if(Zr(A,c,I-c|0),!(ee>>>0>=(m=m+4|0)>>>0))break}if(L&&Zr(A,85998,1),(0|E)<=0|m>>>0>=p>>>0)break i;for(;;){if((c=fs(e[m>>2],0,I))>>>0>o+16>>>0)for(;g[0|(c=c-1|0)]=48,o+16>>>0<c>>>0;);if(Zr(A,c,(0|E)>=9?9:E),c=E-9|0,p>>>0<=(m=m+4|0)>>>0)break s;if(a=(0|E)>9,E=c,!a)break}break s}c:if(!((0|E)<0))for(ee=m>>>0<p>>>0?p:m+4|0,a=8|(i=o+16|0),p=9|i,f=m;;){(0|p)==(0|(c=fs(e[f>>2],0,p)))&&(g[o+24|0]=48,c=a);o:if((0|f)==(0|m))Zr(A,c,1),c=c+1|0,I|E&&Zr(A,85998,1);else{if(o+16>>>0>=c>>>0)break o;for(;g[0|(c=c-1|0)]=48,o+16>>>0<c>>>0;);}if(Zr(A,c,(0|(i=p-c|0))>(0|E)?E:i),E=E-i|0,ee>>>0<=(f=f+4|0)>>>0)break c;if(!((0|E)>=0))break}Aa(A,48,E+18|0,18,0),Zr(A,ve,Qe-ve|0);break r}c=E}Aa(A,48,c+9|0,9,0)}Aa(A,32,r,Z,8192^s),T=(0|r)<(0|Z)?Z:r;break e}if(I=(i<<26>>31&9)+rA|0,!(a>>>0>11)){for(c=12-a|0,ce=16;ce*=16,c=c-1|0;);t=l[0|I]!=45?t+ce-ce:-(ce+(-t-ce))}for((0|Qe)==(0|(c=fs(((c=e[o+44>>2])^(f=c>>31))-f|0,0,Qe)))&&(g[o+15|0]=48,c=o+15|0),ee=2|he,m=32&i,f=e[o+44>>2],g[0|(E=c-2|0)]=i+15,g[c-1|0]=(0|f)<0?45:43,c=8&s,f=o+16|0;i=f,p=Ne(t)<2147483648?~~t:-2147483648,g[0|f]=m|l[p+124512|0],!((0|a)>0|c)&(t=16*(t-+(0|p)))==0|((f=i+1|0)-(o+16|0)|0)!=1||(g[i+1|0]=46,f=i+2|0),t!=0;);T=-1,(2147483645-(i=(c=Qe-E|0)+ee|0)|0)<(0|a)||(Aa(A,32,r,i=(a=!a||((m=f-(o+16|0)|0)-2|0)>=(0|a)?m=f-(o+16|0)|0:a+2|0)+i|0,s),Zr(A,I,ee),Aa(A,48,r,i,65536^s),Zr(A,o+16|0,m),Aa(A,48,a-m|0,0,0),Zr(A,E,c),Aa(A,32,r,i,8192^s),T=(0|r)<(0|i)?i:r)}else Aa(A,32,r,c=he+3|0,-65537&s),Zr(A,rA,he),a=32&i,Zr(A,t!=t?a?85596:85774:a?85247:85460,3),Aa(A,32,r,c,8192^s),T=(0|r)<(0|c)?c:r;return J=o+560|0,0|T},function(A,t){var r;A|=0,r=t|=0,t=e[t>>2]+7&-8,e[r>>2]=t+16,D[A>>3]=ys(e[t>>2],e[t+4>>2],e[t+8>>2],e[t+12>>2])},function(A,t,r){t|=0,r|=0;var a,s,i=0,o=0;return a=e[84+(A|=0)>>2],o=e[a+4>>2],s=e[A+28>>2],(i=(i=e[A+20>>2]-s|0)>>>0>o>>>0?o:i)&&(wt(e[a>>2],s,i),e[a>>2]=i+e[a>>2],o=e[a+4>>2]-i|0,e[a+4>>2]=o),i=e[a>>2],(o=r>>>0>o>>>0?o:r)&&(wt(i,t,o),i=o+e[a>>2]|0,e[a>>2]=i,e[a+4>>2]=e[a+4>>2]-o),g[0|i]=0,t=e[A+44>>2],e[A+28>>2]=t,e[A+20>>2]=t,0|r},function(A,t,r){r|=0;var a,s,i=0;return wt(t|=0,a=e[84+(A|=0)>>2],r=r>>>0>(i=(s=ri(a,0,i=r+256|0))?s-a|0:i)>>>0?i:r),t=a+i|0,e[A+84>>2]=t,e[A+8>>2]=t,e[A+4>>2]=r+a,0|r},function(A,t,r){t|=0,r|=0;var a,s=0,i=0,o=0;s=e[84+(A|=0)>>2],a=e[s>>2]?s:84412,s=0;e:if(e[A+48>>2])for(;;){if(!(i=e[(s<<2)+a>>2]))break e;if(g[e[A+44>>2]+s|0]=(0|i)>=128?64:i,!((s=s+1|0)>>>0<ue[A+48>>2]))break}return i=e[A+44>>2],e[A+4>>2]=i,e[A+84>>2]=(s<<2)+a,e[A+8>>2]=s+i,!r|!s||(e[A+4>>2]=i+1,g[0|t]=l[0|i],o=1),0|o},jn,Ks,D0,D0,function(A,t,r){r|=0;var a,s=0;return J=a=J+-64|0,s=1,_a(A|=0,t|=0,0)||(s=0,t&&(s=0,(t=sr(t,125132))&&(jA(4|(s=a+8|0),0,52),e[a+56>>2]=1,e[a+20>>2]=-1,e[a+16>>2]=A,e[a+8>>2]=t,ct[e[e[t>>2]+28>>2]](t,s,e[r>>2],1),(0|(A=e[a+32>>2]))==1&&(e[r>>2]=e[a+24>>2]),s=(0|A)==1))),J=a- -64|0,0|s},function(A,t,r,a,s,i){r|=0,a|=0,s|=0,i|=0,_a(A|=0,e[8+(t|=0)>>2],i)&&Ii(t,r,a,s)},function(A,t,r,a,s){if(r|=0,a|=0,s|=0,_a(A|=0,e[8+(t|=0)>>2],s))e[t+28>>2]==1|e[t+4>>2]!=(0|r)||(e[t+28>>2]=a);else e:if(_a(A,e[t>>2],s)){if(!(e[t+16>>2]!=(0|r)&e[t+20>>2]!=(0|r))){if((0|a)!=1)break e;return void(e[t+32>>2]=1)}e[t+20>>2]=r,e[t+32>>2]=a,e[t+40>>2]=e[t+40>>2]+1,e[t+36>>2]!=1|e[t+24>>2]!=2||(g[t+54|0]=1),e[t+44>>2]=4}},function(A,t,r,a){r|=0,a|=0,_a(A|=0,e[8+(t|=0)>>2],0)&&w0(t,r,a)},Ks,function(A,t,r,a,s,i){r|=0,a|=0,s|=0,i|=0,_a(A|=0,e[8+(t|=0)>>2],i)?Ii(t,r,a,s):(A=e[A+8>>2],ct[e[e[A>>2]+20>>2]](A,t,r,a,s,i))},function(A,t,r,a,s){if(r|=0,a|=0,s|=0,_a(A|=0,e[8+(t|=0)>>2],s))e[t+28>>2]==1|e[t+4>>2]!=(0|r)||(e[t+28>>2]=a);else e:{if(_a(A,e[t>>2],s)){if(!(e[t+16>>2]!=(0|r)&e[t+20>>2]!=(0|r))){if((0|a)!=1)break e;return void(e[t+32>>2]=1)}e[t+32>>2]=a;A:if(e[t+44>>2]!=4){if(b[t+52>>1]=0,A=e[A+8>>2],ct[e[e[A>>2]+20>>2]](A,t,r,r,1,s),l[t+53|0]){if(e[t+44>>2]=3,!l[t+52|0])break A;break e}e[t+44>>2]=4}if(e[t+20>>2]=r,e[t+40>>2]=e[t+40>>2]+1,e[t+36>>2]!=1|e[t+24>>2]!=2)break e;return void(g[t+54|0]=1)}A=e[A+8>>2],ct[e[e[A>>2]+24>>2]](A,t,r,a,s)}},function(A,t,r,a){r|=0,a|=0,_a(A|=0,e[8+(t|=0)>>2],0)?w0(t,r,a):(A=e[A+8>>2],ct[e[e[A>>2]+28>>2]](A,t,r,a))},Ks,function(A){return 84787},Ks,function(A){return 85058},Ks,function(A){return 84147},function(A){var t;return t=A|=0,A=e[A>>2],e[t>>2]=A+1,0|((0|(A=g[0|A]))<0?65533:255&A)},function(A,t,r,a,s,i){A|=0,t|=0,r|=0,a|=0,s|=0,i|=0;var o,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0;if(o=za(408),e[o+4>>2]=a,e[o>>2]=r,t?(wt(o+16|0,t,376),D[o+392>>3]=(D[t+368>>3]-D[t>>3])/+(r>>>0),t=0):t=1,e[o+400>>2]=s,g[o+8|0]=t,i){if(r=e[A+24>>2])for(t=e[A+20>>2],a=e[A+8>>2];(s=e[e[(t>>>8&16777212)+a>>2]+((1023&t)<<2)>>2])&&(xt(s),r=e[A+24>>2],a=e[A+8>>2],t=e[A+20>>2]),t=t+1|0,e[A+20>>2]=t,r=r-1|0,e[A+24>>2]=r,t>>>0>=2048&&(xt(e[a>>2]),a=e[A+8>>2]+4|0,e[A+8>>2]=a,t=e[A+20>>2]-1024|0,e[A+20>>2]=t,r=e[A+24>>2]),r;);t=e[A+28>>2],e[A+420>>2]=e[t>>2],(r=e[A+32>>2])&&(g[t+8|0]=l[r+8|0],wt(t+16|0,A+40|0,376),(t=e[A+32>>2])&&xt(t),e[A+32>>2]=0)}if(r=(a=e[A+24>>2])+e[A+20>>2]|0,s=e[A+12>>2],(0|r)==(0|((0|(t=e[A+8>>2]))!=(0|s)?(s-t<<8)-1:0))){J=c=J-32|0;e:{A:{r:{s:{if((t=e[16+(i=A+4|0)>>2])>>>0>=1024){if(e[i+16>>2]=t-1024,t=e[i+4>>2],E=e[t>>2],s=t+4|0,e[i+4>>2]=s,(0|(t=e[i+8>>2]))==e[i+12>>2])if((f=e[i>>2])>>>0<s>>>0)r=Yt((a=(1+(s-f>>2)|0)/-2<<2)+s|0,s,t=t-s|0)+t|0,e[i+8>>2]=r,e[i+4>>2]=a+e[i+4>>2];else{if((r=(0|t)==(0|f)?1:t-f>>1)>>>0>=1073741824)break s;if(T=(a=r<<2)+(m=za(a))|0,r=a=m+(-4&r)|0,(0|t)!=(0|s)){if(L=-4&(t=t-s|0),I=1+((p=t-4|0)>>>2|0)&7)for(r=0,t=a;e[t>>2]=e[s>>2],s=s+4|0,t=t+4|0,(0|I)!=(0|(r=r+1|0)););else t=a;if(r=a+L|0,!(p>>>0<28))for(;e[t>>2]=e[s>>2],e[t+4>>2]=e[s+4>>2],e[t+8>>2]=e[s+8>>2],e[t+12>>2]=e[s+12>>2],e[t+16>>2]=e[s+16>>2],e[t+20>>2]=e[s+20>>2],e[t+24>>2]=e[s+24>>2],e[t+28>>2]=e[s+28>>2],s=s+32|0,(0|r)!=(0|(t=t+32|0)););}e[i+12>>2]=T,e[i+8>>2]=r,e[i+4>>2]=a,e[i>>2]=m,f&&(xt(f),r=e[i+8>>2])}else r=t;e[r>>2]=E,e[i+8>>2]=e[i+8>>2]+4;break e}if((f=(s=e[i+8>>2])-e[i+4>>2]>>2)>>>0<(a=(t=e[i+12>>2])-(r=e[i>>2])|0)>>2>>>0){if((0|t)!=(0|s)){e[c+8>>2]=za(4096),Ve(i,c+8|0);break e}if(e[c+8>>2]=za(4096),Be(i,c+8|0),t=e[i+4>>2],E=e[t>>2],s=t+4|0,e[i+4>>2]=s,(0|(t=e[i+8>>2]))==e[i+12>>2])if((f=e[i>>2])>>>0<s>>>0)r=Yt((a=(1+(s-f>>2)|0)/-2<<2)+s|0,s,t=t-s|0)+t|0,e[i+8>>2]=r,e[i+4>>2]=a+e[i+4>>2];else{if((r=(0|t)==(0|f)?1:t-f>>1)>>>0>=1073741824)break s;if(T=(a=r<<2)+(m=za(a))|0,r=a=m+(-4&r)|0,(0|t)!=(0|s)){if(L=-4&(t=t-s|0),I=1+((p=t-4|0)>>>2|0)&7)for(r=0,t=a;e[t>>2]=e[s>>2],s=s+4|0,t=t+4|0,(0|I)!=(0|(r=r+1|0)););else t=a;if(r=a+L|0,!(p>>>0<28))for(;e[t>>2]=e[s>>2],e[t+4>>2]=e[s+4>>2],e[t+8>>2]=e[s+8>>2],e[t+12>>2]=e[s+12>>2],e[t+16>>2]=e[s+16>>2],e[t+20>>2]=e[s+20>>2],e[t+24>>2]=e[s+24>>2],e[t+28>>2]=e[s+28>>2],s=s+32|0,(0|r)!=(0|(t=t+32|0)););}e[i+12>>2]=T,e[i+8>>2]=r,e[i+4>>2]=a,e[i>>2]=m,f&&(xt(f),r=e[i+8>>2])}else r=t;e[r>>2]=E,e[i+8>>2]=e[i+8>>2]+4;break e}if(e[c+24>>2]=i+12,!((t=(0|t)==(0|r)?1:a>>1)>>>0>=1073741824)){if(t=za(r=t<<2),e[c+8>>2]=t,a=t+(f<<2)|0,e[c+16>>2]=a,e[c+20>>2]=t+r,e[c+12>>2]=a,e[c+4>>2]=za(4096),Ve(c+8|0,c+4|0),(0|(s=e[i+8>>2]))==e[i+4>>2]){t=s;break A}for(;Be(c+8|0,s=s-4|0),e[i+4>>2]!=(0|s););break r}}Zi(),xe()}t=e[i+8>>2]}r=e[i>>2],e[i>>2]=e[c+8>>2],e[c+8>>2]=r,e[i+4>>2]=e[c+12>>2],e[c+12>>2]=s,e[i+8>>2]=e[c+16>>2],e[c+16>>2]=t,a=e[i+12>>2],e[i+12>>2]=e[c+20>>2],e[c+20>>2]=a,(0|t)!=(0|s)&&(e[c+16>>2]=t+(3+(s-t|0)&-4)),r&&xt(r)}J=c+32|0,r=(a=e[A+24>>2])+e[A+20>>2]|0,t=e[A+8>>2]}e[e[t+(r>>>8&16777212)>>2]+((1023&r)<<2)>>2]=o,e[A+24>>2]=a+1},function(A){var t=0,r=0,a=0,s=0,i=0,o=0,c=0,f=0,m=0;r=e[420+(A|=0)>>2]+1|0,e[A+420>>2]=r;e:{if(t=e[A+32>>2]){if(r>>>0>(c=e[t+4>>2])>>>0){(r=e[A+28>>2])&&(xt(r),t=e[A+32>>2]),e[A+32>>2]=0,e[A+28>>2]=t;break e}for(i=A+40|0,o=t+16|0,f=e[A+28>>2]+16|0,m=+(r>>>0)/+(c>>>0),t=0;;){if(a=D[(r=t<<3)+o>>3],s=D[r+f>>3],D[r+i>>3]=a==a?(a-s)*m+s:s,(0|(r=1|t))==47)break e;a=D[(r<<=3)+o>>3],s=D[r+f>>3],D[r+i>>3]=a==a?(a-s)*m+s:s,t=t+2|0}}if(t=e[A+28>>2],r>>>0>ue[t>>2]){if(i=e[A+24>>2]){if(g[A+416|0]=0,o=e[A+8>>2],r=e[A+20>>2],t=e[e[o+(r>>>8&16777212)>>2]+((1023&r)<<2)>>2],e[A+32>>2]=t,e[A+24>>2]=i-1,r=r+1|0,e[A+20>>2]=r,r>>>0>=2048&&(xt(e[o>>2]),e[A+8>>2]=e[A+8>>2]+4,e[A+20>>2]=e[A+20>>2]-1024,t=e[A+32>>2]),l[t+8|0])wt(t+16|0,e[A+28>>2]+16|0,376),t=e[A+32>>2],e[t+368>>2]=0,e[t+372>>2]=0,a=D[A+40>>3],e[t+392>>2]=0,e[t+396>>2]=0,D[t+16>>3]=a;else if(r=e[A+28>>2],l[r+8|0]&&(wt(r+16|0,t+16|0,376),t=e[A+28>>2],e[t+368>>2]=0,e[t+372>>2]=0,!(t=e[A+32>>2])))break e;(0|(r=e[t+400>>2]))!=-1&&(e[A+424>>2]=r),e[A+420>>2]=0,D[t+16>>3]=D[t+392>>3]*+ue[t+4>>2]+D[t+16>>3];break e}g[A+416|0]=1}else a=D[t+392>>3]+D[A+40>>3],D[A+40>>3]=a,D[t+16>>3]=a}return 0|(l[A+416|0]?0:A+40)},function(A){return e[424+(A|=0)>>2]},function(A){var t=0;return e[(A|=0)>>2]=132304,(t=e[A+28>>2])&&xt(t),(t=e[A+32>>2])&&xt(t),o0(A+4|0),0|A},function(A){var t=0;e[(A|=0)>>2]=132304,(t=e[A+28>>2])&&xt(t),(t=e[A+32>>2])&&xt(t),o0(A+4|0),xt(A)},function(A,t,r){t|=0,r|=0;var a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0;if(!e[1088+(A|=0)>>2])return 0;e:if(t){for(m=A+648|0,f=A- -64|0;;){if(a=e[A+1088>>2],!(a=0|ct[e[e[a>>2]+4>>2]](a)))break e;if(s=Ys(D[A+32>>3]+D[a+16>>3]/+e[A+24>>2]),D[A+32>>3]=s,s=Oi(6.283185307179586*s),s=Ys(D[A+16>>3]+D[a>>3]*(.06*s*D[a+8>>3]+1)/+e[A+8>>2]),D[A+16>>3]=s,c=It(e[56848],e[56849],1284865837,1481765933),i=$,i=(c=c+1|0)?i:i+1|0,e[56848]=c,e[56849]=i,o=.75*D[A+40>>3]+ +(i>>>1|0)/2147483647,D[A+40>>3]=o,p=D[a+24>>3],i=s>=D[a+32>>3],g[A+48|0]=i,o=(E=p)*(p=.2*o),o=xr(f+512|0,xr(f+440|0,s=D[a+352>>3]*(p*D[a+48>>3]+D[a+40>>3]*(s+s+-1+(i?o:.01*o)))*.5,D[a+104>>3],D[a+168>>3]),D[a+112>>3],D[a+176>>3]),o=xr(f+8|0,xr(f+80|0,xr(f+152|0,xr(f+224|0,xr(f+296|0,xr(f+368|0,o==o?(o-s)*D[a+184>>3]+s:s,D[a+96>>3],D[a+160>>3]),D[a+88>>3],D[a+152>>3]),D[a+80>>3],D[a+144>>3]),D[a+72>>3],D[a+136>>3]),D[a+64>>3],D[a+128>>3]),D[a+56>>3],D[a+120>>3]),c=It(e[56848],e[56849],1284865837,1481765933),i=$,i=(c=c+1|0)?i:i+1|0,e[56848]=c,e[56849]=i,s=.75*D[A+56>>3]+ +(i>>>1|0)/2147483647,D[A+56>>3]=s,i=(I<<1)+r|0,p=xr(m+8|0,s=D[a+352>>3]*(D[a+192>>3]*(.3*s))*.5,D[a+200>>3],D[a+248>>3]),T=D[a+296>>3],L=xr(m+80|0,s,D[a+208>>3],D[a+256>>3]),Z=D[a+304>>3],ee=xr(m+152|0,s,D[a+216>>3],D[a+264>>3]),ce=D[a+312>>3],he=xr(m+224|0,s,D[a+224>>3],D[a+272>>3]),ve=D[a+320>>3],Qe=xr(m+296|0,s,D[a+232>>3],D[a+280>>3]),We=D[a+328>>3],E=o,o=(xr(m+368|0,s,D[a+240>>3],D[a+288>>3])-s)*D[a+336>>3]+(We*(Qe-s)+(ve*(he-s)+(ce*(ee-s)+(Z*(L-s)+(T*(p-s)+0))))),s=(E+(s==s?(s-o)*D[a+344>>3]+o:o))*D[a+360>>3]*4e3,a=(0|(a=Ne(s)<2147483648?~~s:-2147483648))>=32e3?32e3:a,b[i>>1]=(0|a)<=-32e3?-32e3:a,(0|(I=I+1|0))==(0|t))break}I=t}return 0|(t>>>0>I>>>0?I:t)},function(A,t){t|=0,e[1088+(A|=0)>>2]=t},jn,Ks,qi,function(A,t,r,a){return $=0,0}],ui.grow=function(A){var t=this.length;return this.length=this.length+A,t},ui.set=function(A,t){this[A]=t},ui.get=function(A){return this[A]},ui);return{v:function(){var A,t=0;J=A=J-16|0,0|EA(A+12|0,A+8|0)||(t=Ct(4+(e[A+12>>2]<<2)|0),e[56800]=t,t&&(!(t=Ct(e[A+8>>2]))||(e[e[56800]+(e[A+12>>2]<<2)>>2]=0,0|pA(e[56800],0|t)))&&(e[56800]=0)),J=A+16|0,e[56841]=227236,e[56823]=42},w:Mi,x:Vn,y:function(A,t){t|=0,e[(A|=0)>>2]=t},z:function(A,t){return t|=0,g[e[4+(A|=0)>>2]+t|0]},A:ma,B:M0,C:function(A){return l[12+(A|=0)|0]},D:function(A,t){t|=0,g[12+(A|=0)|0]=t},E:function(A){return l[13+(A|=0)|0]},F:function(A,t){t|=0,g[13+(A|=0)|0]=t},G:function(A){return l[14+(A|=0)|0]},H:function(A,t){t|=0,g[14+(A|=0)|0]=t},I:function(A){return l[15+(A|=0)|0]},J:function(A,t){t|=0,g[15+(A|=0)|0]=t},K:Us,L:function(A,t){t|=0,e[16+(A|=0)>>2]=t},M:Wn,N:function(A,t){t|=0,e[20+(A|=0)>>2]=t},O:Mi,P:Vn,Q:G0,R:ma,S:Qs,T:Us,U:Wn,V:function(A){return e[24+(A|=0)>>2]},W:Mi,X:function(){var A,t=0,r=0,a=0,s=0,i=0,o=0,c=0,f=0,m=0,p=0,I=0,E=0,T=0,L=0,Z=0,ee=0,ce=0,he=0,ve=0,Qe=0,We=0,Ke=0,rA=0;if(A=za(20),e[A+16>>2]=0,e[A+8>>2]=175,e[A+12>>2]=50,!(t=e[33208])){J=t=(J=ve=J-16|0)-80|0;e:{if((r=ai(84292))&&(e[t+32>>2]=r,as(137584,160,85959,t+32|0),(0|ss(137584))==-31||(e[t+16>>2]=r,as(137584,160,86031,t+16|0),(0|ss(137584))==-31)))break e;(r=ai(84619))&&(e[t>>2]=r,as(137584,160,85959,t),(0|ss(137584))==-31)||(r=l[84826]|l[84827]<<8|l[84828]<<16|l[84829]<<24,e[34396]=l[84822]|l[84823]<<8|l[84824]<<16|l[84825]<<24,e[34397]=r,b[68804]=l[84846]|l[84847]<<8,r=l[84842]|l[84843]<<8|l[84844]<<16|l[84845]<<24,e[34400]=l[84838]|l[84839]<<8|l[84840]<<16|l[84841]<<24,e[34401]=r,r=l[84834]|l[84835]<<8|l[84836]<<16|l[84837]<<24,e[34398]=l[84830]|l[84831]<<8|l[84832]<<16|l[84833]<<24,e[34399]=r)}if(J=t+80|0,e[ve+12>>2]=0,s=ve+12|0,J=c=J-16|0,e[c+12>>2]=22050,VA(85144)||VA(85315)||VA(85473)||VA(85698),I=c+12|0,J=i=J-16|0,e[i+12>>2]=0,!((t=Si(137832,84262,0,s))||(t=Si(137836,84420,0,s))||(t=Si(137840,84813,0,s))||(t=Si(137820,85016,i+12|0,s))))if(e[34454]=ue[i+12>>2]/68,r=e[34460],e[34456]=r,r&&(0|(m=l[0|r]|l[r+1|0]<<8|l[r+2|0]<<16|l[r+3|0]<<24))==83969){if(E=l[r+4|0]|l[r+5|0]<<8|l[r+6|0]<<16|l[r+7|0]<<24,r=e[34458],L=l[0|r],e[34461]=L,L)for(t=r+4|0,m=0;a=z(m,44)+137856|0,s=l[0|t],e[a+36>>2]=s,e[a+40>>2]=l[t+1|0],o=l[t+8|0]|l[t+9|0]<<8|l[t+10|0]<<16|l[t+11|0]<<24,r=l[t+4|0]|l[t+5|0]<<8|l[t+6|0]<<16|l[t+7|0]<<24,g[0|a]=r,g[a+1|0]=r>>>8,g[a+2|0]=r>>>16,g[a+3|0]=r>>>24,g[a+4|0]=o,g[a+5|0]=o>>>8,g[a+6|0]=o>>>16,g[a+7|0]=o>>>24,o=l[t+16|0]|l[t+17|0]<<8|l[t+18|0]<<16|l[t+19|0]<<24,r=l[t+12|0]|l[t+13|0]<<8|l[t+14|0]<<16|l[t+15|0]<<24,g[a+8|0]=r,g[a+9|0]=r>>>8,g[a+10|0]=r>>>16,g[a+11|0]=r>>>24,g[a+12|0]=o,g[a+13|0]=o>>>8,g[a+14|0]=o>>>16,g[a+15|0]=o>>>24,o=l[t+24|0]|l[t+25|0]<<8|l[t+26|0]<<16|l[t+27|0]<<24,r=l[t+20|0]|l[t+21|0]<<8|l[t+22|0]<<16|l[t+23|0]<<24,g[a+16|0]=r,g[a+17|0]=r>>>8,g[a+18|0]=r>>>16,g[a+19|0]=r>>>24,g[a+20|0]=o,g[a+21|0]=o>>>8,g[a+22|0]=o>>>16,g[a+23|0]=o>>>24,o=l[t+32|0]|l[t+33|0]<<8|l[t+34|0]<<16|l[t+35|0]<<24,r=l[t+28|0]|l[t+29|0]<<8|l[t+30|0]<<16|l[t+31|0]<<24,g[a+24|0]=r,g[a+25|0]=r>>>8,g[a+26|0]=r>>>16,g[a+27|0]=r>>>24,g[a+28|0]=o,g[a+29|0]=o>>>8,g[a+30|0]=o>>>16,g[a+31|0]=o>>>24,r=t+36|0,e[a+32>>2]=r,t=r+(s<<4)|0,(0|L)!=(0|(m=m+1|0)););(0|L)<=e[34457]&&(e[34457]=0),t=0,I&&(e[I>>2]=E)}else e:{A:{if(s){if(r=e[s>>2]){xt(e[r+4>>2]),t=e[s>>2];break A}if(t=Ct(16),e[s>>2]=t,t)break A;t=48}else t=268436223;break e}e[t>>2]=1,e[t+4>>2]=Xi(137584),r=e[s>>2],e[r+12>>2]=83969,e[r+8>>2]=m,t=268436223}if(J=i+16|0,r=t,!t){if(I=e[c+12>>2],e[50754]=I,e[50759]=0,e[50760]=134217728/(0|I),e[50762]=0,e[50763]=0,e[50765]=2147483647,e[50781]=100,e[50779]=32,e[50761]=(I<<6)/(0|I),t=e[26385],e[50784]=e[26384],e[50785]=t,t=e[26387],e[50786]=e[26386],e[50787]=t,t=e[26389],e[50788]=e[26388],e[50789]=t,t=e[26391],e[50790]=e[26390],e[50791]=t,t=e[26393],e[50792]=e[26392],e[50793]=t,t=e[26395],e[50794]=e[26394],e[50795]=t,t=e[26397],e[50796]=e[26396],e[50797]=t,e[50798]=e[26398],E=(0|(t=(0|(s=z(I,60)))/12800|0))>=128?128:t,e[50799]=E,e[50800]=(0|E)/2,!((0|I)==22050|(0|s)<12800)){if(I=1&(t=(0|E)<=1?1:E),ce=+(0|E),m=0,(0|E)>=2)for(E=2147483646&t,t=0;Z=m+132160|0,f=127*(1-Ga(6.283185307179586*+(0|m)/ce)),T=Ne(f)<2147483648?~~f:-2147483648,g[0|Z]=T,Z=132160+(s=1|m)|0,f=127*(1-Ga(6.283185307179586*+(0|s)/ce)),T=Ne(f)<2147483648?~~f:-2147483648,g[0|Z]=T,m=m+2|0,(0|E)!=(0|(t=t+2|0)););I&&(t=m+132160|0,f=127*(1-Ga(6.283185307179586*+(0|m)/ce)),Z=Ne(f)<2147483648?~~f:-2147483648,g[0|t]=Z)}if(e[50801]=105792,e[56797]=ts(),e[55964]=38,e[55921]=1,e[55918]=22050,e[56606]=0,e[55960]=110928,e[55958]=0,e[55959]=1074266112,e[55956]=100,e[55922]=20,e[55923]=220,e[55916]=1,e[55917]=0,ji(),e[56244]=0,e[56245]=0,e[55928]=0,e[55926]=0,e[55927]=0,e[55924]=0,e[56246]=0,e[56247]=0,e[56260]=0,e[56261]=0,e[56262]=0,e[56263]=0,e[56276]=0,e[56277]=0,e[56278]=0,e[56279]=0,e[55974]=0,e[55975]=0,e[55972]=0,e[55973]=0,ce=-3.141592653589793/+(0|(t=e[55918])),D[27967]=ce,s=(0|z(t,630))/1e4|0,e[55920]=s,t=(0|z(t,950))/1e4|0,e[55919]=t,f=-2*ce,D[27968]=f,ce=(Ke=Pt(ce*+(0|s)))*-Ke,D[28129]=ce,f=Ke*Ga(f*+(0|t)),f+=f,D[28128]=f,D[28127]=1-f-ce,e[55990]=0,e[55991]=0,e[55988]=0,e[55989]=0,e[56006]=0,e[56007]=0,e[56004]=0,e[56005]=0,e[56022]=0,e[56023]=0,e[56020]=0,e[56021]=0,e[56038]=0,e[56039]=0,e[56036]=0,e[56037]=0,e[56054]=0,e[56055]=0,e[56052]=0,e[56053]=0,e[56070]=0,e[56071]=0,e[56068]=0,e[56069]=0,e[56086]=0,e[56087]=0,e[56084]=0,e[56085]=0,e[56102]=0,e[56103]=0,e[56100]=0,e[56101]=0,e[56118]=0,e[56119]=0,e[56116]=0,e[56117]=0,e[56134]=0,e[56135]=0,e[56132]=0,e[56133]=0,e[56150]=0,e[56151]=0,e[56148]=0,e[56149]=0,e[56166]=0,e[56167]=0,e[56164]=0,e[56165]=0,e[56182]=0,e[56183]=0,e[56180]=0,e[56181]=0,e[56198]=0,e[56199]=0,e[56196]=0,e[56197]=0,e[56214]=0,e[56215]=0,e[56212]=0,e[56213]=0,e[56230]=0,e[56231]=0,e[56228]=0,e[56229]=0,e[56639]=59,e[56640]=59,e[56629]=0,e[56630]=59,e[56619]=89,e[56620]=160,e[56609]=280,e[56610]=688,e[56611]=1064,e[56621]=70,e[56631]=59,e[56612]=2806,e[56613]=3260,e[56622]=160,e[56623]=200,e[56632]=59,e[56633]=59,e[56641]=89,e[56642]=149,e[56643]=200,e[56644]=200,e[56634]=59,e[56635]=59,e[56624]=200,e[56625]=500,e[56614]=3700,e[56615]=6500,e[56645]=500,e[56646]=0,e[56616]=7e3,e[56626]=500,e[56636]=0,e[56647]=0,e[56637]=0,e[56627]=500,e[56617]=8e3,e[56669]=89,e[56648]=0,e[56638]=0,e[56628]=89,e[56618]=280,e[56657]=62,e[56655]=0,e[56656]=0,e[56653]=50,e[56654]=0,e[56651]=0,e[56652]=0,e[56649]=0,e[56650]=40,e[56607]=1e3,e[56608]=59,J=i=J-416|0,e[i+16>>2]=137584,e[i+20>>2]=47,e[i+24>>2]=85952,DA(t=i+240|0,85699,i+16|0),o=wa(t,86034)){if(Jt(i+240|0,170,o))for(I=5|(t=i+240|0),E=10|t;l[i+240|0]!=47&&(e[i+240>>2]!=1701736308?xa(i+240|0,86614,9)||(e[i+4>>2]=i+32,e[i>>2]=i+239,(0|ut(E,86829,i))==2&&(s=e[34064],e[(L=136272+(s<<4)|0)>>2]=g[i+239|0],t=Xi(i+32|0),e[34064]=s+1,e[L+12>>2]=t,e[L+4>>2]=0)):(J=t=J-48|0,e[32960]=-1,e[32961]=-1,e[32970]=-1,e[32971]=-1,e[32968]=-1,e[32969]=-1,e[32966]=-1,e[32967]=-1,e[32964]=-1,e[32965]=-1,e[32962]=-1,e[32963]=-1,e[t+36>>2]=131876,e[t+32>>2]=131872,e[t+28>>2]=131868,e[t+24>>2]=131864,e[t+20>>2]=131860,e[t+16>>2]=131856,e[t+12>>2]=131852,e[t+8>>2]=131848,e[t+4>>2]=131844,e[t>>2]=131840,ut(I,84222,t),J=t+48|0)),Jt(i+240|0,170,o););ta(o)}J=i+416|0,e[50297]=0,e[50298]=0,e[50301]=0,e[50302]=0,e[50299]=0,e[50300]=0,bi(0,85698),e[36425]=0,e[36424]=0,e[36426]=0,e[36427]=-1,Va(),Kt(0),a=e[25690],e[34062]=a,p=e[25689],i=e[25688],e[34060]=i,e[34061]=p,ee=e[25687],o=e[25686],e[34058]=o,e[34059]=ee,he=e[25685],L=e[25684],e[34056]=L,e[34057]=he,Qe=e[25683],I=e[25682],e[34054]=I,e[34055]=Qe,We=e[25681],E=e[25680],e[34052]=E,e[34053]=We,T=e[25679],s=e[25678],e[34050]=s,e[34051]=T,Z=e[25677],t=e[25676],e[34048]=t,e[34049]=Z,e[33729]=t,e[33730]=Z,e[33731]=s,e[33732]=T,e[33733]=E,e[33734]=We,e[33735]=I,e[33736]=Qe,e[33737]=L,e[33738]=he,e[33739]=o,e[33740]=ee,e[33741]=i,e[33742]=p,e[33743]=a,Cr(1,175),Cr(2,100),Cr(6,e[47200]),Cr(5,e[47201]),Cr(7,0),e[47198]=0,e[47197]=0,f=+Ue()/1e3,s=It(t=Ne(f)<9223372036854776e3?~~f>>>0:0,0,1103515245,0),t=$,t=(s=s+12345|0)>>>0<12345?t+1|0:t,e[33209]=Rn(s,t)}if(J=c+16|0,r){Z=e[30450],Qe=e[ve+12>>2],c=48+(p=J-560|0)|0,J=a=(J=p)-16|0;e:{switch(0|aa(r-268435967|0,24)){case 0:ar(c,84133,512);break e;case 1:ar(c,84580,512);break e;case 2:ar(c,84747,512);break e;case 3:ar(c,85084,512);break e;case 4:ar(c,85251,512);break e;case 5:ar(c,85380,512);break e;case 6:ar(c,85607,512);break e;case 7:ar(c,85722,512);break e;case 8:ar(c,85913,512);break e;case 9:ar(c,86046,512);break e;case 10:ar(c,86153,512);break e;case 11:ar(c,86678,512);break e;case 12:ar(c,86773,512);break e;case 14:ar(c,86958,512);break e;case 15:ar(c,87071,512);break e}if(1879048192&r)e[a>>2]=r,as(c,512,87182,a);else{if(m=0,i=M[123728+((r>>>0<=153?r:0)<<1)>>1]+121804|0,r=e[e[56841]+20>>2]){We=e[r+4>>2],ee=e[r>>2],he=e[ee>>2]+1794895138|0,T=Ms(e[ee+8>>2],he),s=Ms(e[ee+12>>2],he),t=Ms(e[ee+16>>2],he);A:if(!(We>>>2>>>0<=T>>>0||3&(t|s)|(r=We-(T<<2)|0)>>>0<=s>>>0|t>>>0>=r>>>0))for(I=t>>>2|0,E=s>>>2|0;;){if(L=Ms(e[(r=((t=(s=(o=T>>>1|0)+rA|0)<<1)+E<<2)+ee|0)>>2],he),(r=Ms(e[r+4>>2],he))>>>0>=We>>>0|L>>>0>=We-r>>>0|l[(r+L|0)+ee|0])break A;if(!(r=vr(i,r+ee|0))){if(t=Ms(e[(r=(t+I<<2)+ee|0)>>2],he),(r=Ms(e[r+4>>2],he))>>>0>=We>>>0|t>>>0>=We-r>>>0)break A;m=l[(t+r|0)+ee|0]?0:r+ee|0;break A}if((0|T)==1)break A;T=(r=(0|r)<0)?o:T-o|0,rA=r?rA:s}}if((r=HA(t=m||i))>>>0>=512){wt(c,t,511),g[c+511|0]=0;break e}wt(c,t,r+1|0)}}J=a+16|0;e:if(Qe){A:switch(e[Qe>>2]){case 0:e[p+16>>2]=e[Qe+4>>2],e[p+20>>2]=p+48,fr(Z,87384,p+16|0);break e;case 1:break A;default:break e}t=e[Qe+12>>2],r=e[Qe+8>>2],e[p+36>>2]=e[Qe+4>>2],$=r,e[p+40>>2]=t,e[p+44>>2]=$,e[p+32>>2]=p+48,fr(Z,87521,p+32|0)}else e[p>>2]=p+48,fr(Z,87700,p);J=p+560|0,(0|ve)!=-12&&(r=e[ve+12>>2])&&(xt(e[r+4>>2]),xt(e[ve+12>>2]),e[ve+12>>2]=0)}r=e[24806],e[34389]=0,e[32538]=r,r=(1e3+((r=z(e[50754],100))-((0|r)%1e3|0)|0)|0)/500|0,e[34390]=r,r=Ur(e[34391],r),e[34392]=r,r&&(e[34391]=r,e[34393]=40,(r=Ur(e[34388],1440))&&(e[34388]=r)),e[47198]=0,J=ve+16|0,t=e[50754],e[33208]=t}return e[A+4>>2]=t,e[A>>2]=KA(),0|A},Y:function(A,t,r){A|=0,t|=0,r|=0,e[34440]=r,Gn(3,e[A+12>>2]),Gn(1,e[A+8>>2]),(A=e[A+16>>2])?Qa(A):ii(1024),dA(t),e[34440]=0},Z:function(A,t,r){return A|=0,t|=0,r|=0,e[34440]=0,(A=wa(r,1032))?(e[47195]=A,e[47197]=130,A||(e[47195]=e[30450]),dA(t),e[47195]=0,e[47197]=0,e[47195]=e[30450],ta(A),0):-1},_:function(A){return 36},$:function(A,t,r){var a;return A|=0,t|=0,J=a=J-32|0,(r|=0)?(e[a+24>>2]=0,e[a+28>>2]=0,e[a+16>>2]=0,e[a+20>>2]=0,e[a+12>>2]=r,e[a+8>>2]=t,g[a+21|0]=0,t=Qa(a+8|0)):t=ii(t),e[A+16>>2]=201188,J=a+32|0,0|t},aa:function(A,t,r,a){var s;return A|=0,t|=0,J=s=J-32|0,(r|=0)|(a|=0)?(e[s+24>>2]=0,e[s+28>>2]=0,e[s+16>>2]=0,e[s+20>>2]=0,e[s+12>>2]=r,e[s+8>>2]=t,g[s+22|0]=0,g[s+20|0]=a,t=Qa(s+8|0)):t=ii(t),e[A+16>>2]=201188,J=s+32|0,0|t},ba:function(A,t,r,a,s){var i;return A|=0,t|=0,J=i=J-32|0,(a|=0)|(s|=0)|(r|=0)?(e[i+24>>2]=0,e[i+28>>2]=0,e[i+16>>2]=0,e[i+20>>2]=0,e[i+12>>2]=r,e[i+8>>2]=t,g[i+21|0]=s,g[i+20|0]=a,t=Qa(i+8|0)):t=ii(t),e[A+16>>2]=201188,J=i+32|0,0|t},ca:function(A,t,r,a,s,i){var o;return A|=0,t|=0,J=o=J-32|0,(a|=0)|(s|=0)|(i|=0)|(r|=0)?(e[o+24>>2]=0,e[o+28>>2]=0,e[o+16>>2]=0,e[o+20>>2]=0,e[o+12>>2]=r,e[o+8>>2]=t,g[o+22|0]=i,g[o+21|0]=s,g[o+20|0]=a,t=Qa(o+8|0)):t=ii(t),e[A+16>>2]=201188,J=o+32|0,0|t},da:function(A,t){return t|=0,e[e[(A|=0)>>2]+(t<<2)>>2]},ea:function(A,t,r){t|=0,r|=0,e[e[(A|=0)>>2]+(t<<2)>>2]=r},fa:G0,ga:ma,ha:M0,ia:Qs,ja:function(A,t){t|=0,e[12+(A|=0)>>2]=t},ka:Mi,la:function(){return 0},ma:function(){return 1},na:function(){return 2},oa:function(){return 3},pa:function(){return 4},qa:function(){return 5},ra:function(){return 6},sa:function(){return 7},ta:function(){return 8},ua:ct,va:function(){return 227192},wa:xt,xa:Ct,ya:function(A){return(A|=0)?0|!!(0|sr(A,125228)):0}}}(d)}(C)},instantiate:function(x,C){return{then:function(d){var k=new o2.Module(x);d({instance:new o2.Instance(k,C)})}}},RuntimeError:Error};typeof o2!="object"&&a0("no native wasm support detected");var ed=!1;function qc(x,C){x||a0(C)}var ls,r2,tc,mr,ya,Ad,td,ju=typeof TextDecoder<"u"?new TextDecoder("utf8"):void 0;function t2(x,C,d){for(var k=C+d,P=C;x[P]&&!(P>=k);)++P;if(P-C>16&&x.buffer&&ju)return ju.decode(x.subarray(C,P));for(var j="";C<P;){var O=x[C++];if(128&O){var V=63&x[C++];if((224&O)!=192){var q=63&x[C++];if((O=(240&O)==224?(15&O)<<12|V<<6|q:(7&O)<<18|V<<12|q<<6|63&x[C++])<65536)j+=String.fromCharCode(O);else{var R=O-65536;j+=String.fromCharCode(55296|R>>10,56320|1023&R)}}else j+=String.fromCharCode((31&O)<<6|V)}else j+=String.fromCharCode(O)}return j}function a2(x,C){return x?t2(r2,x,C):""}function ig(x,C,d,k){if(!(k>0))return 0;for(var P=d,j=d+k-1,O=0;O<x.length;++O){var 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be;try{be=d()}catch{throw new ae.ErrnoError(29)}if(be===void 0&&H===0)throw new ae.ErrnoError(6);if(be==null)break;H++,q[R+re]=be}return H&&(V.node.timestamp=Date.now()),H},write:(V,q,R,S,X)=>{for(var H=0;H<S;H++)try{k(q[R+H])}catch{throw new ae.ErrnoError(29)}return S&&(V.node.timestamp=Date.now()),H}}),ae.mkdev(P,j,O)},forceLoadFile:x=>{if(x.isDevice||x.isFolder||x.link||x.contents)return!0;throw typeof XMLHttpRequest<"u"?new Error("Lazy loading should have been performed (contents set) in createLazyFile, but it was not. 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ae.ErrnoError(48);return S(X,ls,ye,H,re),{ptr:ye,allocated:!0}},q.stream_ops=R,q},indexedDB:()=>window.indexedDB||window.mozIndexedDB||window.webkitIndexedDB||window.msIndexedDB,DB_NAME:()=>"EM_FS_"+window.location.pathname,DB_VERSION:20,DB_STORE_NAME:"FILE_DATA",saveFilesToDB:(x,C,d)=>{C=C||(()=>{}),d=d||(()=>{});var k=ae.indexedDB();try{var P=k.open(ae.DB_NAME(),ae.DB_VERSION)}catch(j){return d(j)}P.onupgradeneeded=()=>{Ac("creating db"),P.result.createObjectStore(ae.DB_STORE_NAME)},P.onsuccess=()=>{var j=P.result.transaction([ae.DB_STORE_NAME],"readwrite"),O=j.objectStore(ae.DB_STORE_NAME),V=0,q=0,R=x.length;function S(){q==0?C():d()}x.forEach(X=>{var H=O.put(ae.analyzePath(X).object.contents,X);H.onsuccess=()=>{++V+q==R&&S()},H.onerror=()=>{q++,V+q==R&&S()}}),j.onerror=d},P.onerror=d},loadFilesFromDB:(x,C,d)=>{C=C||(()=>{}),d=d||(()=>{});var k=ae.indexedDB();try{var P=k.open(ae.DB_NAME(),ae.DB_VERSION)}catch(j){return d(j)}P.onupgradeneeded=d,P.onsuccess=()=>{var j=P.result;try{var 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`){++P;continue}if(S===k)break;let X=P-1;for(;X>=0&&/\S/.test(d[X]);)X--;X=Math.max(C,X+1);const H=eh(d,X);if(!H){++P;continue}if((/https?[,:]\/\//.test(H)||H.includes("@"))&&!eg(H.at(-1))){P=X+H.length;continue}if(th(H)){++P;continue}if(/^([A-Za-z]\.)+$/.test(H)&&S<k&&/[A-Z]/.test(d[S])){++P;continue}if(V==="."&&S<k&&/[a-z]/.test(d[S])){++P;continue}const re=d.substring(C,R+1).trim();if(re==="..."||re==="…"){++P;continue}re&&this._sentences.push(re),P=C=R+1}else++P}this._buffer=d.substring(C),this._sentences.length>0&&this._resolve()}async*[Symbol.asyncIterator](){if(this._resolver)throw new Error("Another iterator is already active.");for(;;)if(this._sentences.length>0)yield this._sentences.shift();else{if(this._closed)break;await new Promise(C=>{this._resolver=C})}}[Symbol.iterator](){this.flush();const C=this._sentences[Symbol.iterator]();return this._sentences=[],C}get sentences(){return this._sentences}}var Ag={};const I1=Object.freeze({af_heart:{name:"Heart",language:"en-us",gender:"Female",traits:"❤️",targetQuality:"A",overallGrade:"A"},af_alloy:{name:"Alloy",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C"},af_aoede:{name:"Aoede",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C+"},af_bella:{name:"Bella",language:"en-us",gender:"Female",traits:"🔥",targetQuality:"A",overallGrade:"A-"},af_jessica:{name:"Jessica",language:"en-us",gender:"Female",targetQuality:"C",overallGrade:"D"},af_kore:{name:"Kore",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C+"},af_nicole:{name:"Nicole",language:"en-us",gender:"Female",traits:"🎧",targetQuality:"B",overallGrade:"B-"},af_nova:{name:"Nova",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C"},af_river:{name:"River",language:"en-us",gender:"Female",targetQuality:"C",overallGrade:"D"},af_sarah:{name:"Sarah",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C+"},af_sky:{name:"Sky",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C-"},am_adam:{name:"Adam",language:"en-us",gender:"Male",targetQuality:"D",overallGrade:"F+"},am_echo:{name:"Echo",language:"en-us",gender:"Male",targetQuality:"C",overallGrade:"D"},am_eric:{name:"Eric",language:"en-us",gender:"Male",targetQuality:"C",overallGrade:"D"},am_fenrir:{name:"Fenrir",language:"en-us",gender:"Male",targetQuality:"B",overallGrade:"C+"},am_liam:{name:"Liam",language:"en-us",gender:"Male",targetQuality:"C",overallGrade:"D"},am_michael:{name:"Michael",language:"en-us",gender:"Male",targetQuality:"B",overallGrade:"C+"},am_onyx:{name:"Onyx",language:"en-us",gender:"Male",targetQuality:"C",overallGrade:"D"},am_puck:{name:"Puck",language:"en-us",gender:"Male",targetQuality:"B",overallGrade:"C+"},am_santa:{name:"Santa",language:"en-us",gender:"Male",targetQuality:"C",overallGrade:"D-"},bf_emma:{name:"Emma",language:"en-gb",gender:"Female",traits:"🚺",targetQuality:"B",overallGrade:"B-"},bf_isabella:{name:"Isabella",language:"en-gb",gender:"Female",targetQuality:"B",overallGrade:"C"},bm_george:{name:"George",language:"en-gb",gender:"Male",targetQuality:"B",overallGrade:"C"},bm_lewis:{name:"Lewis",language:"en-gb",gender:"Male",targetQuality:"C",overallGrade:"D+"},bf_alice:{name:"Alice",language:"en-gb",gender:"Female",traits:"🚺",targetQuality:"C",overallGrade:"D"},bf_lily:{name:"Lily",language:"en-gb",gender:"Female",traits:"🚺",targetQuality:"C",overallGrade:"D"},bm_daniel:{name:"Daniel",language:"en-gb",gender:"Male",traits:"🚹",targetQuality:"C",overallGrade:"D"},bm_fable:{name:"Fable",language:"en-gb",gender:"Male",traits:"🚹",targetQuality:"B",overallGrade:"C"}}),tg=new Map;async function sh(x){if(tg.has(x))return tg.get(x);const C=new Float32Array(await async function(d){if(Ag?.readFile){const V=typeof __dirname<"u"?__dirname:import.meta.dirname,q=Ag.resolve(V,`../voices/${d}.bin`),{buffer:R}=await Ag.readFile(q);return R}const k=`https://huggingface.co/onnx-community/Kokoro-82M-v1.0-ONNX/resolve/main/voices/${d}.bin`;let P;try{P=await caches.open("kokoro-voices");const V=await P.match(k);if(V)return await V.arrayBuffer()}catch(V){console.warn("Unable to open cache",V)}const j=await fetch(k),O=await j.arrayBuffer();if(P)try{await P.put(k,new Response(O,{headers:j.headers}))}catch(V){console.warn("Unable to cache file",V)}return O}(x));return tg.set(x,C),C}class ng{constructor(C,d){this.model=C,this.tokenizer=d}static async from_pretrained(C,{dtype:d="fp32",device:k=null,progress_callback:P=null}={}){const j=Yp.from_pretrained(C,{progress_callback:P,dtype:d,device:k}),O=Op.from_pretrained(C,{progress_callback:P}),V=await Promise.all([j,O]);return new ng(...V)}get voices(){return I1}list_voices(){console.table(I1)}_validate_voice(C){if(!I1.hasOwnProperty(C))throw console.error(`Voice "${C}" not found. Available voices:`),console.table(I1),new Error(`Voice "${C}" not found. Should be one of: ${Object.keys(I1).join(", ")}.`);return C.at(0)}async generate(C,{voice:d="af_heart",speed:k=1}={}){const P=this._validate_voice(d),j=await qu(C,P),{input_ids:O}=this.tokenizer(j,{truncation:!0});return this.generate_from_ids(O,{voice:d,speed:k})}async generate_from_ids(C,{voice:d="af_heart",speed:k=1}={}){const P=256*Math.min(Math.max(C.dims.at(-1)-2,0),509),j=(await sh(d)).slice(P,P+256),O={input_ids:C,style:new Ru("float32",j,[1,256]),speed:new Ru("float32",[k],[1])},{waveform:V}=await this.model(O);return new Lp(V.data,24e3)}async*stream(C,{voice:d="af_heart",speed:k=1,split_pattern:P=null}={}){const j=this._validate_voice(d);let O;if(C instanceof ag)O=C;else{if(typeof C!="string")throw new Error("Invalid input type. Expected string or TextSplitterStream.");{O=new ag;const V=P?C.split(P).map(q=>q.trim()).filter(q=>q.length>0):[C];O.push(...V)}}for await(const V of O){const q=await qu(V,j),{input_ids:R}=this.tokenizer(q,{truncation:!0}),S=await this.generate_from_ids(R,{voice:d,speed:k});yield{text:V,phonemes:q,audio:S}}}}async function ih(){try{return!!await navigator.gpu.requestAdapter()}catch{return!1}}const sc=await ih()?"webgpu":"wasm";self.postMessage({status:"device",device:sc});const nh="onnx-community/Kokoro-82M-v1.0-ONNX",pf=await ng.from_pretrained(nh,{dtype:sc==="wasm"?"q8":"fp32",device:sc}).catch(x=>{throw self.postMessage({status:"error",error:x.message}),x});self.postMessage({status:"ready",voices:pf.voices,device:sc});self.addEventListener("message",async x=>{const{text:C,voice:d,speed:k}=x.data,P=new ag;P.push(C),P.close();const j=pf.stream(P,{voice:d,speed:k}),O=[];for await(const{text:q,audio:R}of j)self.postMessage({status:"stream",chunk:{audio:R.toBlob(),text:q}}),O.push(R);let V;if(O.length>0){const q=O[0].sampling_rate,R=O.reduce((H,re)=>H+re.audio.length,0),S=new Float32Array(R);let X=0;for(const{audio:H}of O)S.set(H,X),X+=H.length;V=new O[0].constructor(S,q)}self.postMessage({status:"complete",audio:V.toBlob()})});